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    "# Riskfolio-Lib Tutorial: \n",
    "<br>__[Financionerioncios](https://financioneroncios.wordpress.com)__\n",
    "<br>__[Orenji](https://www.orenj-i.net)__\n",
    "<br>__[Riskfolio-Lib](https://riskfolio-lib.readthedocs.io/en/latest/)__\n",
    "<br>__[Dany Cajas](https://www.linkedin.com/in/dany-cajas/)__\n",
    "<a href='https://ko-fi.com/B0B833SXD' target='_blank'><img height='36' style='border:0px;height:36px;' src='https://cdn.ko-fi.com/cdn/kofi1.png?v=2' border='0' alt='Buy Me a Coffee at ko-fi.com' /></a> \n",
    "\n",
    "## Tutorial 2: Portfolio Optimization with Risk Factors using Stepwise Regression\n",
    "\n",
    "## 1. Downloading the data:"
   ]
  },
  {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  30 of 30 completed\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import yfinance as yf\n",
    "import warnings\n",
    "\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "yf.pdr_override()\n",
    "pd.options.display.float_format = '{:.4%}'.format\n",
    "\n",
    "# Date range\n",
    "start = '2016-01-01'\n",
    "end = '2019-12-30'\n",
    "\n",
    "# Tickers of assets\n",
    "assets = ['JCI', 'TGT', 'CMCSA', 'CPB', 'MO', 'APA', 'MMC', 'JPM',\n",
    "          'ZION', 'PSA', 'BAX', 'BMY', 'LUV', 'PCAR', 'TXT', 'TMO',\n",
    "          'DE', 'MSFT', 'HPQ', 'SEE', 'VZ', 'CNP', 'NI', 'T', 'BA']\n",
    "\n",
    "assets.sort()\n",
    "\n",
    "# Tickers of factors\n",
    "\n",
    "factors = ['MTUM', 'QUAL', 'VLUE', 'SIZE', 'USMV']\n",
    "factors.sort()\n",
    "\n",
    "tickers = assets + factors\n",
    "tickers.sort()\n",
    "\n",
    "# Downloading data\n",
    "data = yf.download(tickers, start = start, end = end)\n",
    "data = data.loc[:,('Adj Close', slice(None))]\n",
    "data.columns = tickers"
   ]
  },
  {
   "cell_type": "code",
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     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MTUM</th>\n",
       "      <th>QUAL</th>\n",
       "      <th>SIZE</th>\n",
       "      <th>USMV</th>\n",
       "      <th>VLUE</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>0.4735%</td>\n",
       "      <td>0.2672%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.6779%</td>\n",
       "      <td>0.1635%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>-0.5267%</td>\n",
       "      <td>-1.1914%</td>\n",
       "      <td>-0.5380%</td>\n",
       "      <td>-0.6253%</td>\n",
       "      <td>-1.8277%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>-2.2293%</td>\n",
       "      <td>-2.3798%</td>\n",
       "      <td>-1.7181%</td>\n",
       "      <td>-1.6215%</td>\n",
       "      <td>-2.1609%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>-0.9548%</td>\n",
       "      <td>-1.1377%</td>\n",
       "      <td>-1.1978%</td>\n",
       "      <td>-1.0086%</td>\n",
       "      <td>-1.0873%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>0.6043%</td>\n",
       "      <td>0.1479%</td>\n",
       "      <td>-0.5898%</td>\n",
       "      <td>0.1491%</td>\n",
       "      <td>-0.6183%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               MTUM     QUAL     SIZE     USMV     VLUE\n",
       "Date                                                   \n",
       "2016-01-05  0.4735%  0.2672%  0.0000%  0.6779%  0.1635%\n",
       "2016-01-06 -0.5267% -1.1914% -0.5380% -0.6253% -1.8277%\n",
       "2016-01-07 -2.2293% -2.3798% -1.7181% -1.6215% -2.1609%\n",
       "2016-01-08 -0.9548% -1.1377% -1.1978% -1.0086% -1.0873%\n",
       "2016-01-11  0.6043%  0.1479% -0.5898%  0.1491% -0.6183%"
      ]
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   "source": [
    "# Calculating returns\n",
    "\n",
    "X = data[factors].pct_change().dropna()\n",
    "Y = data[assets].pct_change().dropna()\n",
    "\n",
    "display(X.head())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Estimating Mean Variance Portfolios\n",
    "\n",
    "### 2.1 Estimating the loadings matrix.\n",
    "\n",
    "This part is just to visualize how Riskfolio-Lib calculates a loadings matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
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       "        }</style><table id=\"T_f4fe7_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >const</th>        <th class=\"col_heading level0 col1\" >MTUM</th>        <th class=\"col_heading level0 col2\" >QUAL</th>        <th class=\"col_heading level0 col3\" >SIZE</th>        <th class=\"col_heading level0 col4\" >USMV</th>        <th class=\"col_heading level0 col5\" >VLUE</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row0\" class=\"row_heading level0 row0\" >APA</th>\n",
       "                        <td id=\"T_f4fe7_row0_col0\" class=\"data row0 col0\" >-0.0006</td>\n",
       "                        <td id=\"T_f4fe7_row0_col1\" class=\"data row0 col1\" >-0.6551</td>\n",
       "                        <td id=\"T_f4fe7_row0_col2\" class=\"data row0 col2\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row0_col3\" class=\"data row0 col3\" >0.9406</td>\n",
       "                        <td id=\"T_f4fe7_row0_col4\" class=\"data row0 col4\" >-0.7883</td>\n",
       "                        <td id=\"T_f4fe7_row0_col5\" class=\"data row0 col5\" >1.7237</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row1\" class=\"row_heading level0 row1\" >BA</th>\n",
       "                        <td id=\"T_f4fe7_row1_col0\" class=\"data row1 col0\" >0.0005</td>\n",
       "                        <td id=\"T_f4fe7_row1_col1\" class=\"data row1 col1\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row1_col2\" class=\"data row1 col2\" >1.1744</td>\n",
       "                        <td id=\"T_f4fe7_row1_col3\" class=\"data row1 col3\" >0.3616</td>\n",
       "                        <td id=\"T_f4fe7_row1_col4\" class=\"data row1 col4\" >-0.4322</td>\n",
       "                        <td id=\"T_f4fe7_row1_col5\" class=\"data row1 col5\" >0.0000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row2\" class=\"row_heading level0 row2\" >BAX</th>\n",
       "                        <td id=\"T_f4fe7_row2_col0\" class=\"data row2 col0\" >0.0003</td>\n",
       "                        <td id=\"T_f4fe7_row2_col1\" class=\"data row2 col1\" >0.3146</td>\n",
       "                        <td id=\"T_f4fe7_row2_col2\" class=\"data row2 col2\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row2_col3\" class=\"data row2 col3\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row2_col4\" class=\"data row2 col4\" >0.7717</td>\n",
       "                        <td id=\"T_f4fe7_row2_col5\" class=\"data row2 col5\" >0.0000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row3\" class=\"row_heading level0 row3\" >BMY</th>\n",
       "                        <td id=\"T_f4fe7_row3_col0\" class=\"data row3 col0\" >-0.0003</td>\n",
       "                        <td id=\"T_f4fe7_row3_col1\" class=\"data row3 col1\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row3_col2\" class=\"data row3 col2\" >0.8123</td>\n",
       "                        <td id=\"T_f4fe7_row3_col3\" class=\"data row3 col3\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row3_col4\" class=\"data row3 col4\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row3_col5\" class=\"data row3 col5\" >0.0000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row4\" class=\"row_heading level0 row4\" >CMCSA</th>\n",
       "                        <td id=\"T_f4fe7_row4_col0\" class=\"data row4 col0\" >0.0001</td>\n",
       "                        <td id=\"T_f4fe7_row4_col1\" class=\"data row4 col1\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row4_col2\" class=\"data row4 col2\" >0.4958</td>\n",
       "                        <td id=\"T_f4fe7_row4_col3\" class=\"data row4 col3\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row4_col4\" class=\"data row4 col4\" >0.4962</td>\n",
       "                        <td id=\"T_f4fe7_row4_col5\" class=\"data row4 col5\" >0.0000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row5\" class=\"row_heading level0 row5\" >CNP</th>\n",
       "                        <td id=\"T_f4fe7_row5_col0\" class=\"data row5 col0\" >0.0001</td>\n",
       "                        <td id=\"T_f4fe7_row5_col1\" class=\"data row5 col1\" >-0.5595</td>\n",
       "                        <td id=\"T_f4fe7_row5_col2\" class=\"data row5 col2\" >-0.2157</td>\n",
       "                        <td id=\"T_f4fe7_row5_col3\" class=\"data row5 col3\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row5_col4\" class=\"data row5 col4\" >1.8341</td>\n",
       "                        <td id=\"T_f4fe7_row5_col5\" class=\"data row5 col5\" >0.0000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row6\" class=\"row_heading level0 row6\" >CPB</th>\n",
       "                        <td id=\"T_f4fe7_row6_col0\" class=\"data row6 col0\" >-0.0003</td>\n",
       "                        <td id=\"T_f4fe7_row6_col1\" class=\"data row6 col1\" >-0.4782</td>\n",
       "                        <td id=\"T_f4fe7_row6_col2\" class=\"data row6 col2\" >-0.5994</td>\n",
       "                        <td id=\"T_f4fe7_row6_col3\" class=\"data row6 col3\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row6_col4\" class=\"data row6 col4\" >2.0793</td>\n",
       "                        <td id=\"T_f4fe7_row6_col5\" class=\"data row6 col5\" >0.0000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row7\" class=\"row_heading level0 row7\" >DE</th>\n",
       "                        <td id=\"T_f4fe7_row7_col0\" class=\"data row7 col0\" >0.0004</td>\n",
       "                        <td id=\"T_f4fe7_row7_col1\" class=\"data row7 col1\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row7_col2\" class=\"data row7 col2\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row7_col3\" class=\"data row7 col3\" >0.3631</td>\n",
       "                        <td id=\"T_f4fe7_row7_col4\" class=\"data row7 col4\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row7_col5\" class=\"data row7 col5\" >0.8090</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row8\" class=\"row_heading level0 row8\" >HPQ</th>\n",
       "                        <td id=\"T_f4fe7_row8_col0\" class=\"data row8 col0\" >0.0002</td>\n",
       "                        <td id=\"T_f4fe7_row8_col1\" class=\"data row8 col1\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row8_col2\" class=\"data row8 col2\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row8_col3\" class=\"data row8 col3\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row8_col4\" class=\"data row8 col4\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row8_col5\" class=\"data row8 col5\" >1.2514</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row9\" class=\"row_heading level0 row9\" >JCI</th>\n",
       "                        <td id=\"T_f4fe7_row9_col0\" class=\"data row9 col0\" >0.0001</td>\n",
       "                        <td id=\"T_f4fe7_row9_col1\" class=\"data row9 col1\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row9_col2\" class=\"data row9 col2\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row9_col3\" class=\"data row9 col3\" >0.3412</td>\n",
       "                        <td id=\"T_f4fe7_row9_col4\" class=\"data row9 col4\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row9_col5\" class=\"data row9 col5\" >0.5797</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row10\" class=\"row_heading level0 row10\" >JPM</th>\n",
       "                        <td id=\"T_f4fe7_row10_col0\" class=\"data row10 col0\" >0.0005</td>\n",
       "                        <td id=\"T_f4fe7_row10_col1\" class=\"data row10 col1\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row10_col2\" class=\"data row10 col2\" >0.3077</td>\n",
       "                        <td id=\"T_f4fe7_row10_col3\" class=\"data row10 col3\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row10_col4\" class=\"data row10 col4\" >-0.5485</td>\n",
       "                        <td id=\"T_f4fe7_row10_col5\" class=\"data row10 col5\" >1.1512</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row11\" class=\"row_heading level0 row11\" >LUV</th>\n",
       "                        <td id=\"T_f4fe7_row11_col0\" class=\"data row11 col0\" >-0.0001</td>\n",
       "                        <td id=\"T_f4fe7_row11_col1\" class=\"data row11 col1\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row11_col2\" class=\"data row11 col2\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row11_col3\" class=\"data row11 col3\" >0.3642</td>\n",
       "                        <td id=\"T_f4fe7_row11_col4\" class=\"data row11 col4\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row11_col5\" class=\"data row11 col5\" >0.6767</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row12\" class=\"row_heading level0 row12\" >MMC</th>\n",
       "                        <td id=\"T_f4fe7_row12_col0\" class=\"data row12 col0\" >0.0003</td>\n",
       "                        <td id=\"T_f4fe7_row12_col1\" class=\"data row12 col1\" >-0.2693</td>\n",
       "                        <td id=\"T_f4fe7_row12_col2\" class=\"data row12 col2\" >0.6808</td>\n",
       "                        <td id=\"T_f4fe7_row12_col3\" class=\"data row12 col3\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row12_col4\" class=\"data row12 col4\" >0.6066</td>\n",
       "                        <td id=\"T_f4fe7_row12_col5\" class=\"data row12 col5\" >0.0000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row13\" class=\"row_heading level0 row13\" >MO</th>\n",
       "                        <td id=\"T_f4fe7_row13_col0\" class=\"data row13 col0\" >-0.0004</td>\n",
       "                        <td id=\"T_f4fe7_row13_col1\" class=\"data row13 col1\" >-0.4572</td>\n",
       "                        <td id=\"T_f4fe7_row13_col2\" class=\"data row13 col2\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row13_col3\" class=\"data row13 col3\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row13_col4\" class=\"data row13 col4\" >1.4359</td>\n",
       "                        <td id=\"T_f4fe7_row13_col5\" class=\"data row13 col5\" >0.0000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row14\" class=\"row_heading level0 row14\" >MSFT</th>\n",
       "                        <td id=\"T_f4fe7_row14_col0\" class=\"data row14 col0\" >0.0005</td>\n",
       "                        <td id=\"T_f4fe7_row14_col1\" class=\"data row14 col1\" >1.0302</td>\n",
       "                        <td id=\"T_f4fe7_row14_col2\" class=\"data row14 col2\" >0.6531</td>\n",
       "                        <td id=\"T_f4fe7_row14_col3\" class=\"data row14 col3\" >-0.2640</td>\n",
       "                        <td id=\"T_f4fe7_row14_col4\" class=\"data row14 col4\" >-0.2595</td>\n",
       "                        <td id=\"T_f4fe7_row14_col5\" class=\"data row14 col5\" >0.0000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row15\" class=\"row_heading level0 row15\" >NI</th>\n",
       "                        <td id=\"T_f4fe7_row15_col0\" class=\"data row15 col0\" >-0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row15_col1\" class=\"data row15 col1\" >-0.4648</td>\n",
       "                        <td id=\"T_f4fe7_row15_col2\" class=\"data row15 col2\" >-0.4612</td>\n",
       "                        <td id=\"T_f4fe7_row15_col3\" class=\"data row15 col3\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row15_col4\" class=\"data row15 col4\" >2.3257</td>\n",
       "                        <td id=\"T_f4fe7_row15_col5\" class=\"data row15 col5\" >-0.3532</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row16\" class=\"row_heading level0 row16\" >PCAR</th>\n",
       "                        <td id=\"T_f4fe7_row16_col0\" class=\"data row16 col0\" >0.0003</td>\n",
       "                        <td id=\"T_f4fe7_row16_col1\" class=\"data row16 col1\" >-0.4420</td>\n",
       "                        <td id=\"T_f4fe7_row16_col2\" class=\"data row16 col2\" >1.0319</td>\n",
       "                        <td id=\"T_f4fe7_row16_col3\" class=\"data row16 col3\" >0.2217</td>\n",
       "                        <td id=\"T_f4fe7_row16_col4\" class=\"data row16 col4\" >-0.4831</td>\n",
       "                        <td id=\"T_f4fe7_row16_col5\" class=\"data row16 col5\" >0.7536</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row17\" class=\"row_heading level0 row17\" >PSA</th>\n",
       "                        <td id=\"T_f4fe7_row17_col0\" class=\"data row17 col0\" >-0.0004</td>\n",
       "                        <td id=\"T_f4fe7_row17_col1\" class=\"data row17 col1\" >-0.3311</td>\n",
       "                        <td id=\"T_f4fe7_row17_col2\" class=\"data row17 col2\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row17_col3\" class=\"data row17 col3\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row17_col4\" class=\"data row17 col4\" >1.7073</td>\n",
       "                        <td id=\"T_f4fe7_row17_col5\" class=\"data row17 col5\" >-0.4915</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row18\" class=\"row_heading level0 row18\" >SEE</th>\n",
       "                        <td id=\"T_f4fe7_row18_col0\" class=\"data row18 col0\" >-0.0005</td>\n",
       "                        <td id=\"T_f4fe7_row18_col1\" class=\"data row18 col1\" >-0.3267</td>\n",
       "                        <td id=\"T_f4fe7_row18_col2\" class=\"data row18 col2\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row18_col3\" class=\"data row18 col3\" >0.2595</td>\n",
       "                        <td id=\"T_f4fe7_row18_col4\" class=\"data row18 col4\" >0.6973</td>\n",
       "                        <td id=\"T_f4fe7_row18_col5\" class=\"data row18 col5\" >0.4801</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row19\" class=\"row_heading level0 row19\" >T</th>\n",
       "                        <td id=\"T_f4fe7_row19_col0\" class=\"data row19 col0\" >-0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row19_col1\" class=\"data row19 col1\" >-0.4521</td>\n",
       "                        <td id=\"T_f4fe7_row19_col2\" class=\"data row19 col2\" >-0.3719</td>\n",
       "                        <td id=\"T_f4fe7_row19_col3\" class=\"data row19 col3\" >-0.2951</td>\n",
       "                        <td id=\"T_f4fe7_row19_col4\" class=\"data row19 col4\" >1.3089</td>\n",
       "                        <td id=\"T_f4fe7_row19_col5\" class=\"data row19 col5\" >0.7620</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row20\" class=\"row_heading level0 row20\" >TGT</th>\n",
       "                        <td id=\"T_f4fe7_row20_col0\" class=\"data row20 col0\" >0.0005</td>\n",
       "                        <td id=\"T_f4fe7_row20_col1\" class=\"data row20 col1\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row20_col2\" class=\"data row20 col2\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row20_col3\" class=\"data row20 col3\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row20_col4\" class=\"data row20 col4\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row20_col5\" class=\"data row20 col5\" >0.7562</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row21\" class=\"row_heading level0 row21\" >TMO</th>\n",
       "                        <td id=\"T_f4fe7_row21_col0\" class=\"data row21 col0\" >0.0002</td>\n",
       "                        <td id=\"T_f4fe7_row21_col1\" class=\"data row21 col1\" >0.2867</td>\n",
       "                        <td id=\"T_f4fe7_row21_col2\" class=\"data row21 col2\" >0.5400</td>\n",
       "                        <td id=\"T_f4fe7_row21_col3\" class=\"data row21 col3\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row21_col4\" class=\"data row21 col4\" >0.3864</td>\n",
       "                        <td id=\"T_f4fe7_row21_col5\" class=\"data row21 col5\" >0.0000</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row22\" class=\"row_heading level0 row22\" >TXT</th>\n",
       "                        <td id=\"T_f4fe7_row22_col0\" class=\"data row22 col0\" >-0.0003</td>\n",
       "                        <td id=\"T_f4fe7_row22_col1\" class=\"data row22 col1\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row22_col2\" class=\"data row22 col2\" >0.4485</td>\n",
       "                        <td id=\"T_f4fe7_row22_col3\" class=\"data row22 col3\" >0.4429</td>\n",
       "                        <td id=\"T_f4fe7_row22_col4\" class=\"data row22 col4\" >-0.6363</td>\n",
       "                        <td id=\"T_f4fe7_row22_col5\" class=\"data row22 col5\" >0.8108</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row23\" class=\"row_heading level0 row23\" >VZ</th>\n",
       "                        <td id=\"T_f4fe7_row23_col0\" class=\"data row23 col0\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row23_col1\" class=\"data row23 col1\" >-0.5099</td>\n",
       "                        <td id=\"T_f4fe7_row23_col2\" class=\"data row23 col2\" >-0.5271</td>\n",
       "                        <td id=\"T_f4fe7_row23_col3\" class=\"data row23 col3\" >-0.2886</td>\n",
       "                        <td id=\"T_f4fe7_row23_col4\" class=\"data row23 col4\" >1.8941</td>\n",
       "                        <td id=\"T_f4fe7_row23_col5\" class=\"data row23 col5\" >0.4321</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_f4fe7_level0_row24\" class=\"row_heading level0 row24\" >ZION</th>\n",
       "                        <td id=\"T_f4fe7_row24_col0\" class=\"data row24 col0\" >0.0004</td>\n",
       "                        <td id=\"T_f4fe7_row24_col1\" class=\"data row24 col1\" >-0.2371</td>\n",
       "                        <td id=\"T_f4fe7_row24_col2\" class=\"data row24 col2\" >0.0000</td>\n",
       "                        <td id=\"T_f4fe7_row24_col3\" class=\"data row24 col3\" >0.3553</td>\n",
       "                        <td id=\"T_f4fe7_row24_col4\" class=\"data row24 col4\" >-0.8203</td>\n",
       "                        <td id=\"T_f4fe7_row24_col5\" class=\"data row24 col5\" >1.6431</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f8cfe093640>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import riskfolio.ParamsEstimation as pe\n",
    "\n",
    "step = 'Forward' # Could be Forward or Backward stepwise regression\n",
    "loadings = pe.loadings_matrix(X=X, Y=Y, stepwise=step)\n",
    "\n",
    "loadings.style.format(\"{:.4f}\").background_gradient(cmap='RdYlGn')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.2 Calculating the portfolio that maximizes Sharpe ratio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.9509%</td>\n",
       "      <td>11.8691%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>9.7091%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.3628%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>10.4797%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.5105%</td>\n",
       "      <td>1.3012%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.0981%</td>\n",
       "      <td>0.0000%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            APA      BA      BAX     BMY   CMCSA     CNP     CPB      DE  \\\n",
       "weights 0.0000% 5.9509% 11.8691% 0.0000% 0.0000% 9.7091% 0.0000% 4.3628%   \n",
       "\n",
       "            HPQ     JCI  ...       NI    PCAR     PSA     SEE       T     TGT  \\\n",
       "weights 0.0000% 0.0000%  ... 10.4797% 0.0000% 0.0000% 0.0000% 0.0000% 5.5105%   \n",
       "\n",
       "            TMO     TXT      VZ    ZION  \n",
       "weights 1.3012% 0.0000% 4.0981% 0.0000%  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import riskfolio.Portfolio as pf\n",
    "\n",
    "# Building the portfolio object\n",
    "port = pf.Portfolio(returns=Y)\n",
    "\n",
    "# Calculating optimum portfolio\n",
    "\n",
    "# Select method and estimate input parameters:\n",
    "\n",
    "method_mu='hist' # Method to estimate expected returns based on historical data.\n",
    "method_cov='hist' # Method to estimate covariance matrix based on historical data.\n",
    "\n",
    "port.assets_stats(method_mu=method_mu, method_cov=method_cov, d=0.94)\n",
    "\n",
    "port.factors = X\n",
    "port.factors_stats(method_mu=method_mu, method_cov=method_cov, d=0.94)\n",
    "\n",
    "# Estimate optimal portfolio:\n",
    "\n",
    "port.alpha = 0.05\n",
    "model='FM' # Factor Model\n",
    "rm = 'MV' # Risk measure used, this time will be variance\n",
    "obj = 'Sharpe' # Objective function, could be MinRisk, MaxRet, Utility or Sharpe\n",
    "hist = False # Use historical scenarios for risk measures that depend on scenarios\n",
    "rf = 0 # Risk free rate\n",
    "l = 0 # Risk aversion factor, only useful when obj is 'Utility'\n",
    "\n",
    "w = port.optimization(model=model, rm=rm, obj=obj, rf=rf, l=l, hist=hist)\n",
    "\n",
    "display(w.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Plotting portfolio composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import riskfolio.PlotFunctions as plf\n",
    "\n",
    "# Plotting the composition of the portfolio\n",
    "\n",
    "ax = plf.plot_pie(w=w, title='Sharpe FM Mean Variance', others=0.05, nrow=25, cmap = \"tab20\",\n",
    "                  height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2.3 Calculate efficient frontier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.0025%</td>\n",
       "      <td>4.4251%</td>\n",
       "      <td>3.6052%</td>\n",
       "      <td>8.5001%</td>\n",
       "      <td>5.0116%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.4407%</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0999%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>13.3270%</td>\n",
       "      <td>1.0131%</td>\n",
       "      <td>8.1911%</td>\n",
       "      <td>4.0880%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>10.3193%</td>\n",
       "      <td>1.6740%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.6551%</td>\n",
       "      <td>6.2745%</td>\n",
       "      <td>1.3152%</td>\n",
       "      <td>3.4741%</td>\n",
       "      <td>10.4973%</td>\n",
       "      <td>3.1812%</td>\n",
       "      <td>0.4168%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.6580%</td>\n",
       "      <td>...</td>\n",
       "      <td>13.7898%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>7.8658%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.6866%</td>\n",
       "      <td>4.9570%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>10.7069%</td>\n",
       "      <td>1.7274%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.2940%</td>\n",
       "      <td>7.3296%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.1085%</td>\n",
       "      <td>11.2072%</td>\n",
       "      <td>2.5027%</td>\n",
       "      <td>1.0040%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.7576%</td>\n",
       "      <td>...</td>\n",
       "      <td>14.4136%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.7433%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.6756%</td>\n",
       "      <td>5.1835%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>10.8397%</td>\n",
       "      <td>1.5227%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.8323%</td>\n",
       "      <td>8.2192%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.6402%</td>\n",
       "      <td>11.7336%</td>\n",
       "      <td>1.7740%</td>\n",
       "      <td>1.5147%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.7924%</td>\n",
       "      <td>...</td>\n",
       "      <td>14.8329%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.5115%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.5658%</td>\n",
       "      <td>5.3546%</td>\n",
       "      <td>0.0914%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>10.8121%</td>\n",
       "      <td>1.2525%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.2845%</td>\n",
       "      <td>8.9511%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.1579%</td>\n",
       "      <td>12.1063%</td>\n",
       "      <td>1.0660%</td>\n",
       "      <td>1.9452%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0882%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.3878%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.4816%</td>\n",
       "      <td>5.4886%</td>\n",
       "      <td>0.4619%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>10.6844%</td>\n",
       "      <td>0.9777%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      APA      BA     BAX     BMY   CMCSA      CNP     CPB      DE     HPQ  \\\n",
       "0 0.0000% 0.0000% 2.0025% 4.4251% 3.6052%  8.5001% 5.0116% 0.0000% 0.0000%   \n",
       "1 0.0000% 1.6551% 6.2745% 1.3152% 3.4741% 10.4973% 3.1812% 0.4168% 0.0000%   \n",
       "2 0.0000% 2.2940% 7.3296% 0.0000% 3.1085% 11.2072% 2.5027% 1.0040% 0.0000%   \n",
       "3 0.0000% 2.8323% 8.2192% 0.0000% 2.6402% 11.7336% 1.7740% 1.5147% 0.0000%   \n",
       "4 0.0000% 3.2845% 8.9511% 0.0000% 2.1579% 12.1063% 1.0660% 1.9452% 0.0000%   \n",
       "\n",
       "      JCI  ...       NI    PCAR      PSA     SEE       T     TGT     TMO  \\\n",
       "0 4.4407%  ... 12.0999% 0.0000% 13.3270% 1.0131% 8.1911% 4.0880% 0.0000%   \n",
       "1 2.6580%  ... 13.7898% 0.0000%  7.8658% 0.0000% 5.6866% 4.9570% 0.0000%   \n",
       "2 1.7576%  ... 14.4136% 0.0000%  5.7433% 0.0000% 4.6756% 5.1835% 0.0000%   \n",
       "3 0.7924%  ... 14.8329% 0.0000%  3.5115% 0.0000% 3.5658% 5.3546% 0.0914%   \n",
       "4 0.0000%  ... 15.0882% 0.0000%  1.3878% 0.0000% 2.4816% 5.4886% 0.4619%   \n",
       "\n",
       "      TXT       VZ    ZION  \n",
       "0 0.0000% 10.3193% 1.6740%  \n",
       "1 0.0000% 10.7069% 1.7274%  \n",
       "2 0.0000% 10.8397% 1.5227%  \n",
       "3 0.0000% 10.8121% 1.2525%  \n",
       "4 0.0000% 10.6844% 0.9777%  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "points = 50 # Number of points of the frontier\n",
    "\n",
    "frontier = port.efficient_frontier(model=model, rm=rm, points=points, rf=rf, hist=hist)\n",
    "\n",
    "display(frontier.T.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting the efficient frontier\n",
    "\n",
    "label = 'Max Risk Adjusted Return Portfolio' # Title of point\n",
    "mu = port.mu_fm # Expected returns\n",
    "cov = port.cov_fm # Covariance matrix\n",
    "returns = port.returns_fm # Returns of the assets\n",
    "\n",
    "ax = plf.plot_frontier(w_frontier=frontier, mu=mu, cov=cov, returns=returns, rm=rm,\n",
    "                       rf=rf, alpha=0.05, cmap='viridis', w=w, label=label,\n",
    "                       marker='*', s=16, c='r', height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting efficient frontier composition\n",
    "\n",
    "ax = plf.plot_frontier_area(w_frontier=frontier, cmap=\"tab20\", height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Optimization with Constraints on Risk Factors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.1 Statistics of Risk Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "const    -0.0583%\n",
       "MTUM    -65.5126%\n",
       "QUAL    -59.9357%\n",
       "SIZE    -29.5069%\n",
       "USMV    -82.0320%\n",
       "VLUE    -49.1496%\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "const     0.0504%\n",
       "MTUM    103.0208%\n",
       "QUAL    117.4359%\n",
       "SIZE     94.0623%\n",
       "USMV    232.5683%\n",
       "VLUE    172.3685%\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>MTUM</th>\n",
       "      <th>QUAL</th>\n",
       "      <th>SIZE</th>\n",
       "      <th>USMV</th>\n",
       "      <th>VLUE</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>MTUM</th>\n",
       "      <td>100.0000%</td>\n",
       "      <td>90.4265%</td>\n",
       "      <td>79.1172%</td>\n",
       "      <td>87.2321%</td>\n",
       "      <td>78.5395%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>QUAL</th>\n",
       "      <td>90.4265%</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>89.8168%</td>\n",
       "      <td>89.9554%</td>\n",
       "      <td>91.6580%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SIZE</th>\n",
       "      <td>79.1172%</td>\n",
       "      <td>89.8168%</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>82.5080%</td>\n",
       "      <td>87.9111%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>USMV</th>\n",
       "      <td>87.2321%</td>\n",
       "      <td>89.9554%</td>\n",
       "      <td>82.5080%</td>\n",
       "      <td>100.0000%</td>\n",
       "      <td>76.9678%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>VLUE</th>\n",
       "      <td>78.5395%</td>\n",
       "      <td>91.6580%</td>\n",
       "      <td>87.9111%</td>\n",
       "      <td>76.9678%</td>\n",
       "      <td>100.0000%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          MTUM      QUAL      SIZE      USMV      VLUE\n",
       "MTUM 100.0000%  90.4265%  79.1172%  87.2321%  78.5395%\n",
       "QUAL  90.4265% 100.0000%  89.8168%  89.9554%  91.6580%\n",
       "SIZE  79.1172%  89.8168% 100.0000%  82.5080%  87.9111%\n",
       "USMV  87.2321%  89.9554%  82.5080% 100.0000%  76.9678%\n",
       "VLUE  78.5395%  91.6580%  87.9111%  76.9678% 100.0000%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Displaying factors statistics\n",
    "\n",
    "display(loadings.min())\n",
    "display(loadings.max())\n",
    "display(X.corr())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.2 Creating Constraints on Risk Factors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Disabled</th>\n",
       "      <th>Factor</th>\n",
       "      <th>Sign</th>\n",
       "      <th>Value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>MTUM</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>-30.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>QUAL</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>80.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>SIZE</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>40.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>USMV</td>\n",
       "      <td>&gt;=</td>\n",
       "      <td>80.0000%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>VLUE</td>\n",
       "      <td>&lt;=</td>\n",
       "      <td>90.0000%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Disabled Factor Sign     Value\n",
       "0     False   MTUM   <= -30.0000%\n",
       "1     False   QUAL   <=  80.0000%\n",
       "2     False   SIZE   <=  40.0000%\n",
       "3     False   USMV   >=  80.0000%\n",
       "4     False   VLUE   <=  90.0000%"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Creating risk factors constraints\n",
    "\n",
    "import riskfolio.ConstraintsFunctions as cf\n",
    "\n",
    "constraints = {'Disabled': [False, False, False, False, False],\n",
    "               'Factor': ['MTUM', 'QUAL', 'SIZE', 'USMV', 'VLUE'],\n",
    "               'Sign': ['<=', '<=', '<=', '>=', '<='],\n",
    "               'Value': [-0.3, 0.8, 0.4, 0.8 , 0.9],}\n",
    "\n",
    "constraints = pd.DataFrame(constraints)\n",
    "\n",
    "display(constraints)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "C, D = cf.factors_constraints(constraints, loadings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>weights</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.5883%</td>\n",
       "      <td>1.4644%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>17.5476%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.0956%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>...</td>\n",
       "      <td>14.0140%</td>\n",
       "      <td>1.4169%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.4188%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>10.6393%</td>\n",
       "      <td>2.1181%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            APA      BA     BAX     BMY   CMCSA      CNP     CPB      DE  \\\n",
       "weights 0.0000% 6.5883% 1.4644% 0.0000% 0.0000% 17.5476% 0.0000% 4.0956%   \n",
       "\n",
       "            HPQ     JCI  ...       NI    PCAR     PSA     SEE       T     TGT  \\\n",
       "weights 0.0000% 0.0000%  ... 14.0140% 1.4169% 0.0000% 0.0000% 0.0000% 4.4188%   \n",
       "\n",
       "            TMO     TXT       VZ    ZION  \n",
       "weights 0.0000% 0.0000% 10.6393% 2.1181%  \n",
       "\n",
       "[1 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "port.ainequality = C\n",
    "port.binequality = D\n",
    "\n",
    "w = port.optimization(model=model, rm=rm, obj=obj, rf=rf, l=l, hist=hist)\n",
    "\n",
    "display(w.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To check if the constraints are verified, I will make a regression among the portfolio returns and risk factors:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "const     0.0229%\n",
      "MTUM    -30.0000%\n",
      "QUAL     15.4395%\n",
      "SIZE      1.8657%\n",
      "USMV     92.6045%\n",
      "VLUE     21.8622%\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "import statsmodels.api as sm\n",
    "\n",
    "X1 = sm.add_constant(X)\n",
    "y = np.matrix(returns) * np.matrix(w)\n",
    "results = sm.OLS(y, X1).fit()\n",
    "coefs = results.params\n",
    "\n",
    "print(coefs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.3 Plotting portfolio composition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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vu90LL7yAfv36oUOHDpctv9gHubS++uor3H333dBqC29kq1KlyqV/jxo1Cs8999xV20yYMAFTp07F9OnTMWTIENSqVQsvv/wyvvzyy6u2FUJ8XuqwzKeI6HcA84losb8WP/mtxLcZzUF99QZjT6MpSNumY3dNq/a36xs2awe9weSX3SRuRG5OFkjWlOuNdbIxGKb4NjDFt9GGdUaI4nbClXaovuPErgTbgfXD3RmndLIxaJ3isC4CsEoIkVqe+RhjrKLjAvkKXbp0wQsvvIBPP/30Uh/jzZs3w2azoWbNmpe2S0xMRFJSEr777ju0bt3aZ+dfuHAhXn/99SLXp6amonr16gCAFStWoEGDBpet/+OPPxAdHY2EhATYbDZIkgRZlmGz2XyWkZUNIcQ6ItoDYDSAD9TOA1xqJW4kSXJPkzlooEara5iQ1MzRrvMdQc3b3UrVY+tctxtPoMu1ZgGyVtWRJyStHobYRjDENpJCb7432GvLgv3oP7fZDqxv6zi67UPJYDkhPM6v4PWsALBFCKGomZcxxgIdF8hXICJ88803eOKJJzBt2jQYDIZLw7xdacKECWjWrFmJjr9582bcfffduHDhAlauXInJkydjz549APJuAjx58iQ6dux42T6TJk1Cy5Yt0bt3b0yfPh0rVqyARqNBeHg4vvjii0vbCSEwdepUfPXVVwCA0aNHY8iQIfB4PJg5c2bJXgimlgkAfiCiz4UQVjUC5BfFLfVG00i9wTTAYDLr23ToLre8pZuhUfObYDCaK1wr8bVYs7MgZP/qNi2bQmBp2BmWhp0tQvHCeXpfgu3QxmdtB9Y/rtiyhKQ3LRcu+2cA/uRimTHGSo54SE4gKSnpcCWcavrI3r1749XOwa5GRAsB7BFCTC3n89aUNZphOp3hIYPRFHpb7/sM7bvdLUfXjK/wrcTX8u2XH2HJsoVK1WHvlUkfZF9zZ6bBtn+dYt3xY64394IbQswVHtcXyBsZg//gM8ZYMXALMmP+ZyKAjUQ0UwiRXpYnIqJQAP3MQSFjDEZTg1u63o1b77zXUK9Ri0pdFBdkzc4UitYYEMUxAGhDqyGkbT8ppG2/INe5Y8jd/duj1l2/jBZezzmSNZ9C8c4XQhxXOydjjPkzLpAZ8zNCiMNEtBTAcwCe9fXx88cb7m6yBD+s1eq6NG7Z3t31rvsszW+6FVofDWdWkWRlnFckY5Csdo7S0EXWgq7zSG1op/u1ztP7aubu+vXF3H1rX5QNlgOKM/dDAAvV6srDGGP+jAtkxvzTKwB2EdF/hRCnfXFAIqpnMJrH63SGwdE145Vudw8LvqlLLwSFhOmvv3fllZV5XsjGYLVj3BAiCYaYhjDENDSEd3sE9qP/JOf88/27zpO735d0xrnC7XhfCHFA7ZyMMeYvuEAGYLVaTyQlJakdo1xZrdYTamdgRRNCnCai2QAmAXiotMfJv+Guk8kSPNFosrTr0W+k5rbeQzTVomted1+Wx5qdSVJQxblFgWTtxSHkLJ7ss8j5Z9UDOdtWDZcMlh3CmfsGgO94Fj/GWGXHN+kx5qeIKBzAQQDthBCHSrivHsAgkzloojkoJOqe4ePMHbv3I73BWCZZK7LH7+2g5NbrKgU366F2lDIjPG7kHliH7L+X5Xgy05zC634fivcTIcQ5tbMxxpgauEBmzI8R0QQAjYUQg4q5fYRGq3tUluUnatdrrOl3/xOWpm06QZIC5h4zv/NAr6ZC02E0mRJ8N965P3OmHUbOluV224H1BEmzUrhsk4UQ+9TOxRhj5YkLZMb8GBFZABwC0FMIse0a2zUwmizPezzuATd16SX6DHnUWDO+QVGbsxIYclsCQvq/Cn1UHbWjlCuvPQfWbd97s/7+2gXCGuG0TRBCbFc7F2OMlQcukBnzc0T0GIAeQoiehay7yWQJngYhWt45cJS2+z0jNKHhkSqkrJiEEOh/SwyiH/sSksGidhxVKC4HrNtXKVl/LXYKiI3CafuPEOJvtXMxxlhZ4gKZMT+X3594P4DBQogN+ctamMxB72q0upZDHv6PsWP3fqTTG9QNWgE5HTYMuS0BsU8vVzuK6oTHBevOn0TmugV2oXh2CqfteSHEH2rnYoyxssAFMmMBgIiaATgHIMxkCX5bIqn9vaOf09/We7Ck1fEobWUl41waxgxoh+rjlqgdxW8Irxu5e9aIzD/n2xS346Bw2p4RQvyqdi7GGPMlHuaNscCQZTRbPiDQ7QNGPqW//e7hEo9IUfasOZmQNVoBgKcVzEeyFpYm3cjc6FZz7r61zTJ//3y5pDfvEC7b2Gv1k2eMsUDCBTJjfoyIIvUG0yt6g3F473sf1vS+9xGN0WRWO1alkZuTBUnmArkwJMmwNOwMc2J7s3XHj20vrJ23XtKbVwmX7WkhxDG18zHG2I3gApkxP0REZo1WN16nNzzbsUc/edCDzxj45rvyl5uTBcga7od2DSRrENT8TsncsIsxe9PXd2Vv+qanpDN8LNzOyUKIbLXzMcZYaXCBzJgfyZ/5boDeYPwouVVHw/DHJpuqx9ZWO1alZc3JAvJakNl1SHoTQtvfp7E066nJXPO/h2wHN4wgSX4OQpkthPCqnY8xxkqCC2TG/AQR1TGag74IDg1vPm7SDHNik1ZqR6r0cnOyIGQ9d68oAY0lHBG9njY60w4bM1Z/+LY749SzRDRCCLFW7WyMMVZcPL0WYyojIp1Op5+oNxh33zPs8Zs+WLSOi2M/kZN9QXg1elntHIFIXy0e1Ya9a6nSY1wdyRD0o6Q3zSOiMLVzMcZYcXALMmMqIqIOBqN5Xr2GzauMeeEdY9XqcWpHYgVkZ2Yokt7CBXIpERHMibfAWLuZ8cKaz/vl7lnTi4geAbBI8BijjDE/xgUyYyogogijyTI9KCTsrkeef9vUpmNP5HU/Zv4k68J5IZmC1Y4R8CS9GVW6jzVYGt9mOP/d2596bVmPENFwIcRRtbMxxlhhuIsFY+WIiEiSpPt1euORTj369521bLOpbac7uDj2UzmZGZBNIWrHqDD00Ymo8eAsc0jb/u1Io99NGu3zRKRVOxdjjF2JW5AZKydEVN9otsyPqFoj8fFJH1jqJiarHYldhzUnk+TYBmrHqFBI1iCk3QCNKbG9Jn3V+y+6zv77ABHdK4TYonY2xhi7iFuQGStjREQajXaU3mD8595RzzV/d94aLo4DRG5OliQHVVE7RoWkDauOqMHTzOHdHqlLOuNaSaObRETc35sx5he4BZmxMkRE4UaTZV5oeGTH59/8whRbu77akVgJ2G1WMlu4QC4rRARLwy5kiG1sPLd82rPu8yfuIqK+QojjamdjjFVu3ILMWBkhog56g/FApx79b31v/hozF8eBx2G3QRNcVe0YFZ4mOBLVhrxpDmk3oAlp9LuJpHvVzsQYq9y4QGbMx4hIozeYppkswT8+/eqnEaOefl2v0xvUjsVKyONxw+txQ7KEqx2lUiBJRkjb/pqoIW9Y5OCITyW9aSkR8R2SjDFVcIHMmA8RUW2jybI1vkHy2A8WrTO2uOk2tSOxUsrNyYJGq4Mk8Z/J8qSvFo8aD840mxPb30FawwEiulntTIyxyof/8jPmI5IkDdbpjbsGPDC+4ZQPvzaHVeGv5gNZrjUbskbHk1moQNIaUKXH44aIXs9Ekc70s6TVTyUivl4xxsoN/8Fh7AYRUZDJHLS4StUan7z28QrzXYMfkbnVMfDlZmdC0mi4QFaRKaENaoyaZdRG1HyCdMafiShU7UyMscqBr+KM3QAiqmMwmne2an977+kL15rr1G+sdiTmI9acLJCs5QJZZRpLOKrd95bZ3LDzzaQ17CaiJLUzMcYqPi6QGSslIuqo0xu3DXn4P7HjJs8wGIxmtSMxH8q1ZgEytyD7A5I1qNJtjD6868M1SKPfRER91c7EGKvYuEBmrBQ0Gu1og8n8w/NvfhF8x4AHZZ4quuLJzc6CkHVqx2AFWBrfRlGDp5klU8g8SWt4kycWYYyVFS6QGSsBItIYTeaZoVUi33vrf6uNTVt3VDsSKyPWnCwosp4LMD+jr56AGg98ZNJWrTWGdMbfiChM7UyMsYqHC2TGiomIQowmy2+16zUe9t68NabomvFqR2JlKCcrQ4HezF8N+CHZFIJqQ940Wxrd1oa0+r1E1FDtTIyxioULZMaKgYgSDEbzzg639209ZcYykyU4VO1IrIxlXTgvJGOQ2jFYEUiSEd71IX141zFRpNFvIKL2amdijFUcXCAzdh1E1EWnN24dNnZizEPPvqmXNRq1I7FykJWZDtnIE7n5O0vjWymy74tBpDX8SCT1UzsPY6xi4AKZsWvQaHWPGE2W7ya8PS+oe9/7+felErFmZ0Iyh6odgxWDsXYzVBvyhkkymOeSRjtO7TyMscDHF3zGiqA3GCeFhFZ5+60vfjI2bnmL2nFYOcvNySSNJVztGKyYdFF1Uf3+/xplc9hrks7wHs+8xxi7EfwHhLErEBEZjKa3QqtUfe7Nz1ebasTWUTsSU4HNmkOypYraMVgJaEKiUP3+/5o0YdGjSGf8ioh4nD7GWKlwgcxYAUQk6Y2mjyKioh9587MfTOERUWpHYipx2HNJDo5UOwYrIdkYjGr3vWnWRzfoQTrjGiIKVjsTYyzwcIHMWD4ikg1G89wasXWHTvv0e3NwKLceVlaKosDldEATUlXtKKwUJK0BVftNNpkSb2lOOuNGHiuZMVZSXCAzBoCItAaTeVnN+KS7p8781mwO4tELKjO7zQpZ1kDS8Df0gYokGVW6P26wNO5al3TGv4iIO5QzxoqNC2RW6eV1ObZ8X69h864vf7DEZDSZ1Y7EVJabkwlZy8VxoCMihN06Smdp0rUOablIZowVHxfIrFIjIpPRZPmlUYubb5nwzpcmnd6gdiTmB3JzsiFrtIraOdiNIyKEdRmlsyR3q0064wYukhljxcEFMqu0iCjYaLKsbXlL1xbPvvaZUcsthiyfNScTkswTwlQUeUXygzpLcvdapDVuICK+wYAxdk1cILNKiYgsRpNl3S1d+zQaN/lDA8+OxwrKzckCZG5BrkiICGGdR+qCmnWvRVrDBiKKUDsTY8x/cYHMKh0i0hlNlh9ad+ie8PBzb+kliX8N2OVyc7IgZK3aMZiPERFCO43UBTXrWZOLZMbYtXBlwCoVIpKMJsvixCatmo+d8L6BiNSOxPyQNScLiqzjN0cFlFckj9AFNb8jjrSGdTxOMmOsMPy9Mqs0iIj0BtNH0bUSuj77+v9M3K2CFSUn+wIUrYkbECooIkJox/t1Xnt2Tdv+dT8SUSchhEvtXOUtLi7uN4vFEqd2DjVZrdYTJ06c6KJ2DuZ/uEJglYZOb5gUHhl13+T3F5n1BqPacZgfy76Q7pUMQbLaOVjZISJUuX2swWvNSHae3LuIiPoJISpVv3OLxRK3d+/eumrnUFNSUpLaEZif4hYSVilotbqHzZaQZ1/5iCcBYdeXdSFdyMYgtWOwMkaSjMg+L5i0VaK7kdbwX7XzMMb8BxfIrMKTJKmv3mh6d+rMb03hEVFqx2EBICcrgyRzqNoxWDmQtHpUHTjVLJtCR5BG95zaeRhj/oELZFahEVFnvcE07+UPlhqrx9ZWOw4LENacLJLNPJ9EZSEbLIga/LpZ0hknkSQPVTuPWogIQ4f+/9P3eDyIjIzEnXfeCQD44osvQET49ddfL23zzTffgIiwdOlSAIDb7cbzzz+PhIQENGrUCK1bt8YPP/xw1bmGDBmC+vXro1GjRhg5ciTcbjcAICsrC7169UJycjIaNmyIzz//vNCsM2bMQHx8PIgI58+fv7R82bJlaNiwIdq3b4/09HQAwJEjRzBo0KAbfHVYZcMFMquwiKiZ3mBc+cLbc0116jdWOw4LIDZrNskWLpArE01wJKLufd1EWv3HRNRN7TxqMJvN2L17N+x2OwDg559/RnR09GXbNG7cGAsXLrz086JFi5CcnHzp54kTJyI1NRW7d+/G7t27sXLlSuTk5Fx1riFDhmD//v3YtWsX7HY7Zs+eDQD48MMPkZSUhB07duD333/H+PHj4XJdff/kzTffjF9++QU1a9a8bPk777yDjRs3YtiwYViwYAEA4MUXX8Qrr7xSyleFVVZcILMKiYjidHrjb49PmmFq1PxmteOwAGO3WUkTHKl2DFbOdJE1UbX/S0bS6L8moqZq51FDjx498P333wMAFi5ciHvvvfey9e3bt8emTZvgdrthtVpx+PBhNG3aFABgs9nw6aef4oMPPoBerwcAREVFYcCAAVedp2fPniAiEBFat26NU6dOAchrxc7JyYEQAlarFeHh4dAUMuJQs2bNUKtWrauWS5IEp9MJm80GrVaLP//8E9WrV0dCQsKNvCysEuJRLFiFQ0QGo8ny4z3Dx1nadb6Dx7JlJeZ02CBzgVwpGWIaokqPx03pP05fTUQNhRDnr79X8RFRDIB7oV4DlRXAzKJG7Bg0aBCmTJmCO++8Ezt37sTIkSPx559/XlpPRLjtttuwevVqZGVloXfv3jh69CgA4PDhw4iLi0NwcPGHlna73Zg3bx7++9+8eyTHjh2L3r17o0aNGsjJycHixYtRksmcJk+ejNtvvx01atTA/PnzMWDAACxatKjY+zN2EbcgswqFiMhgMv+vUYuba909dCx/AGQl5nTYAQHwKBaVlzmpI1ma9gwlnXElEfns7wgR9QWwFUACgHAVH0Vq0qQJjh07hoULF6Jnz56FbjNo0CAsWrQIixYtuqqFuaTGjBmDDh06oH379gCA1atXo2nTpkhJScH27dsxduxYZGdnF/t4Xbt2xdatW7Fy5Up8++236NmzJw4cOIB+/fph1KhRsNlsN5SXVR5cQLAKRdZox4SGRfZ+4qWPjDxLHiuN3JwsaHQ68PuncgvrdL/OlXaoiSvt8PsAxt7IsYjIDOA9ALcCuEsIsdEHEctM79698fTTT+P333+/dKNbQa1bt8bu3bthNBpRr169S8vj4+Nx4sQJ5OTkICjo+h8wX375ZZw7dw4ff/zxpWWff/45nn/+eRAR4uPjUbt2bezfvx+tW7cu0XOw2WyYM2cOVq9ejW7dumH58uVYsGABvvzyS4waNapEx2KVE7cgswqDiG7WanVvTXxvodloMqsdhwWoXGsWZFkr1M7B1EWSjMi7J5gkvWkESfKwUh+HqDnyWo0NAJr5e3EMACNHjsSkSZPQuHHRNze//vrreO211y5bZjKZ8MADD+Dxxx+/dGNdamoq5s+ff9X+s2fPxurVq7Fw4cLLulDExcVdGiXjzJkzOHDgAOrUqVPi5/Dmm29i3Lhx0Gq1sNvtICJIksQtyKzYuEBmFQIR1dAZjCufemUWD+fGbog1OwuShgtkltfNpuqAKSbS6GYSUcuS7EtEEhGNB/AjgJeFEMOEEMXvK6CimJgYjBs37prb9OjRA507d75q+dSpUxEZGYmkpCQ0atQIffr0QWTk1f35H374YZw5cwbt2rVD06ZNMWXKFAB5o2D89ddfaNy4MW699Va88cYbiIiIAJB3Y19KSgoAYPr06YiJicGpU6fQpEkTPPjgg5eOnZKSgi1btuCuu+4CAIwfPx5t27bFnDlzMHjw4NK9KKzSISH4OsACGxHpjCbLpt73Ppw08MGntWrnYYFty7qfMP31Z7xVRn3GU00zAIDtwF84//0754Xb2UgIceZ62xNRdQBzAJgBDBFCHCvrjKWRlJR0mKeaTjqyd+/eeLVzMP/DLcgs4BmM5lmJTVol9B/5FBfH7IblWrMhJG5BZv/PVP8mBLXoHUI64/dEdM2/M0TUC8A2AOsBdPTX4pgxdm18kx4LaBqN9sGwiKiB46d+YirJUECs8vB43LBmZ8LpsEEIAEJACAEBcenfkiTDaLbAaDLDmpMJodHxHXrsMqEdhmpdqYcaOFP2vwvgsSvXE5ERwNsAegK4RwixvrwzMsZ8hwtkFrCIqLXeaJo+6f2FRpOZh+SqbDweN86mnEDKyX+RduoYTp847Lxw7owr68J5JTsrA7k5WbLdZtV53G6NVqfLlWTZCQAEEgDyHnkjVQgIIXm9XqPH4zIqXq+GZA1OzRyRLRtDhGwJI9kSrpMtVfSa0CjSRdaGtkosSMNfWFQmRBIi7nrWlPLpwyOJaJUQ4of/X0dNACwEsBN5N+JlqpWTMeYb3AeZBSQiCtIbTAcfnzS9WrvOd6odh5Uhr9eLU8cO4vDebTi8b7vz+JH9jrRTR+XszAyj3mBM12i1R90u1y6HPXcvgDMA0vMfGfn/zRYl+ENHRDoAYQCqAoi89F+iKElvbiqE0kS4ndVlS7hNV7UO6asnWLRVa5Mushbk4Ko8PFwF5zi5G2e/mpwlPM4GQohUIhoLYDKA8QDmleS9pra4uLjfLBZLnNo51GS1Wk+cOHGii9o5mP/hApkFJKPJMqd1h+79n3jpQ6PaWZhvZZxLw8E9/+Dgnq2e3VvX5x4/ss+o0WgvSLK8MTcnay2AAwAOATgmhHCpkZGI9AAaAGgMWdtc0hraCK+rAYQwacJq2PXV6+l11RMMhlrNoA2tpkZEVoYy/5zvzt6yfKtw2TsAmAngdSHEEbVzMcZ8hwtkFnCIqGdIWMSSD5dsMHHXisDncjqwd/tGbF73k+vvP1a5crIukN5g3GHPtf7q9Xo2ANgkhLh6tgI/RERVADQG0IR0pvZQPN0kYzCZ6t9sMMW30epjkkAy92wLdELxIm3+M7mus0dfEx7Xa9ffgzEWaLhAZgGFiKroDcZDE96ZH9ao+c1qx2GldDb1BLb+9Ss2rvk+Z//OTXqdwXjQbrMuUbze7wFsE0Ioamf0BSKSADSHpLlL0uoHCK+7pqFmU7ep/s0WY50WkM2hakdkpeTJPouU2WPswu24WQixTe08jDHf4gKZBQwiIqPJsqLzHQO7PvjUq3q187CSuZB+Fn+u/lqs/nauNf1sCjRa3WqbNXsZgJ+EEBlq5ysPRFQNQE9Jbx6oeFwdtKHVXaYG7c3Guq1kXVRd7r8cYHL3/C7SV884IdyOBkIIu9p5GGO+wwUyCxiSJN0bUS3m0+kL1pr1Bu56HAgc9lz8vfZH/PTt3JzDe7drtFrdCltuzicA/hBCeNXOp6b8mwHbk0bfF5LUVzIEWYJb9zVbGnUhSW9SOx4rpnPfvGa3H/1nnuKyP6R2FsaY73CBzAICEcXo9MZ9r8761lI3MVntOOwahBA4sGsLvl8y27b5z9WyVqffnJuT9RGA5UIIm9r5/BHlNR13JL35WXg9nc0NO1FQy7v0uohKPcBAQFAcVpz+ZLRNsWf3EkL8pnYexphvcIHM/F5+14o/e9/7cGueStp/eTxubFzzPZZ+8X7O2dSTNqfD/pYQyvziTM3L/h8RxZCsHQOSxmgja0nBbfoGmRLagiSe+dpf2Q5vwvkVb6YItyOeu1owVjFwgcz8nkarGxtdM37aO1/8bJY1PAKAv8nNycJPy+cp387/yOH1evbbrNmvAFhZ2btQ3Kj8Lhh9SW9+nogSglr01gU17aGRLWFqR2OFOPv1VJvj6LaPFLfjGbWzMMZuHBfIzK8RUYJOb9z+zpyfTdE149WOwwo4fzYFX8+Z7vzt+0VCo9H+YMvNeVUIsVXtXBURESWTzvgUvJ7+hjotldBbBpt1VWurHYsV4LVewOlPR9uFy95WCLFT7TyMsRvDBTLzW0RERrNl86AHn23aa9Bo/n7ZT1xIP4uln7/n/PW7hQoRfZLXlUKcVjtXZUBEoZDkUSTJE43xreXQjvebeCIS/5GzY7W48NvsfcJlb8LfoDAW2LhAZn5LkqShNeLiZ77/5e9mWeb6WG251mwsm/Nf96ol//OQRHOcdtvLQog0tXNVRkQUTBrdswCeNDe6VQ69ZbBeNnPXC7UJIZA2b3yu68y/E4XX/Z7aeRhjpccFMvNLRBSqNxiPTpmxLDShYXO141RqbpcTPy77Qlk0+y0ngOV2m/VZIcRJtXMxgIgiSWt4GULcH9z6bk1w235aSWtQO1al5s44jdTPH7MJjytJCHFc7TyMsdLhApn5JaPJ/MlNXXoPHfvi+3y1V9GOTWsxY+o4m91m3WzLzRkrhNitdiZ2NSKqRTrTByRru4R3fdhkSryFJx1RUeb6hZ7sTd+sEy5bF8EXWcYCEhfIzO8QUXOTOWjdzGV/G4NCwtWOUyllZpzD7HdesG/961er02EbKYT4Tu1M7PqIqCPpjLO14THVqnR/zKKLqqN2pEpJeN1Imf1IriczbaQQ4iu18zDGSk5SOwBjBRGRFGyQv6kSEakn4rdneVMUBT99O1eM6d/WvnXDrx87HbbaXBwHDiHEH8JlT3SdOfJM2vync9J/+sipuJ1qx6p0SNaiSs8nzKQ1zCAi/haMsQDEFQjzKwQMrmEWkUmaE2JM7yTx5azXoSiK2rEqheOH9+Hp+7vmzpnxyi6HLbeNw5b7pBAiV+1crGSEEF6heGcJj6tm7p41q1M/G5PrOvuv2rEqHUNsI+hjGpoga55SOwtjrOS4iwXzG0RkMWlx4pehprB2sRr8fMSDB1Y4hF0yK49M+lhu1q6L2hErJEVRsHzBTO9Xn71td7tdzyhe7ydCCP5UUgEQEYGkoSRrPwppP8QQ3KqPzN/MlJ/8G/ZyhcdVRwhxVu08jLHi4wKZ+Q2Tlt64s55m7Ff9TaaLy1xegfc2uJQpa51SXHxj7zNvzpfDIqLUjFmhZJw/g3deHJ177NCew3abtY8Q4pjamZjvEVFt0hm/0VWtHR/R+zmzJqiK2pEqjYyfZzmtu375UnHZH1A7C2Os+LhAZn6BiGqZtNh7cKzFGB18dQtXao6CcT86vN8f8srtew3HqPGvQZK4JexGbFn3E95/eazd43a953I6JgshPGpnYmWHiDSk0U2CpHk64o4njKZ6N6kdqVLw2nNwetZIu3DZWwkh9qidhzFWPFwgM78QrKdFj7XR9X21i0F7re3+OunBiOUOcd6lFyOefV+6+ba7yitiheFyOvD5fyc7f/9hSY7TYesrhPhT7Uys/BBRO9IavjbVvzk0vOsjBknH95CVtezN3yqZ6xasU5y5HdXOwhgrHi6QmeqIqH6QDttOPhlkDDFcf+xWryLwyT8u8dzPToqIruN9+s0v5eoxtcshaeA7m3oCU8YNyr2QfnaN3WYdJoS4oHYmVv6IKIh0xk8lvfnOyLsnmPXVE9SOVKEJrxunZz2Q67Vm9BVC/KR2HsbY9fF31Ex1IXq89fwtel1ximMAkCXCIy31dOyJIHQJPYmnB9+E9yc/IlwuVxknDWz7dvyNp4bdZj+Xdmqy3WbtzcVx5SWEyFGctkHenPSRZxY8b7Xu/IlvyixDJGsR3vVhM+mMs4hIVjsPY+z6uEBmqiKiZAC3jWujK/FFI9xImN3bKP810oScnSvEQz3qip++mVcGKQPfr98tFC+PG2S1WbP7ulzOd3h2LwYAQihfCY+zZcYvH5/LXLfAzW+LsmNMaAdtWI1IAEPUzsIYuz7uYsFUFWqgX1/qpO/0RFv9DX1YE0JgwS4PHv/RAVNYde+T0+bJtRMa+ipmwPJ6vfhi+mTXLysWpDsdtluFEPvUzsT8DxFVI63xd1PiLTWrdB9rIIkbOcuC/dh2nPvm1dPCZa8phPCqnYcxVjRuQWaqIaI2GonaPtxSd8PvQyLCkCZaHBtnQf+4c3hx5K14/ekhit1eeee5sOXmYMoTA22/fbfoH6fD1piLY1YUIUSacNtb2Q6s23J26cs2xe1QO1KFZKiZDE1o9RAAA9TOwhi7Ni6QmWpC9Hj/1Vv1RoOmeH2PiyNIT3j3dqO87SEzjKd+x8M964llcz/w2fEDRdaF83huZA/boT3bltht1g5CiHS1MzH/JoTIES77rc7T+75Pm/9MrteWpXakCoeIENZxuIV0xteJZ2xhzK/xLyhTBRF1Mmqp8cimWt9VxwXUj5Dx+3CTNL+Pjn6Z+5oY0yvJu3fHxrI4ld/JOJeG5x7saTt/9vRHDnvuCCGEW+1MLDAIIVzCZR/ouZAyK/WLcTZ3ZprakSocQ+3m0ARHVgHQV+0sjLGicR9kpopQA22d3sPQfFiyrszP5fAIvP6nU3nrL5cU36i18vS0OVJQSFiZn1cNZ1NP4oXRvWzWnMw3nA77FLXzsMBFGu1jktbwRtWBU436avFqx6lQbIc34fzKt/8VLlsCT+vOmH/iFmRW7oiotU6mxMGNrzkniM8YNISXOxukfY9aEJv7jxjTu6GY++ErUJSKdV1KOXEEz47sbsvJypjIxTG7UcLj/kBxWIecWfC8zX70H7XjVCjGuq0gW8KqAuildhbGWOG4QGblLkSPic/fojNopDLpXVGkmqESvh9skpcPNNC2lTPFwz3rebeu/6VcM5SV40f24fkH77Dn5mSNc7mc76qdh1UMQohvhNtx+7lvXrXaj21XO06F8f99kU1vElH5/iFkjBULd7Fg5YqIapm02Jc2PsgQpFfvuuD2Ckz/26VM/t0pRddJ8j795nw5omoN1fLciJQTR/DsAz3s9tycBxRFWah2HlbxEFEH0hp+qDbkDZMuqq7acSoEIRSkfDI615OZNlAI8b3aeRhjl+MWZFauLFo8+3ALnaxmcQwAWpkw/ia9dORxC5prD+KJfi0wc9rTwusNrKFJM86lYcIjfWxOu+0xLo5ZWRFCrBVu59AziybwjXs+QiQhtP19ZklvfkntLIyxq3GBzMoNEYV6BO5/sp2ufDofF0OURcLCe0zyr8OMOLV+gRjdva7yx+plascqFmt2Jl58pE+u3Zrzmsfj/kztPKxiE0L5WnE5njnz5XM2b26m2nEqBFP9mwGgIRE1UjsLY+xyXCCzcqOV8HCvehoRE+x/b7u2MRrsHmOWpnUS9PnrY/HUoLbe1JP/qh2rSE6HDS891j/3QsbZz10ux2tq52GVg/C6P1Ic1ulnFk3IVVx2teMEPJK1CGrRW0s643i1szDGLsd9kFm5ICKdSYu09SPNYU2r+fc0thfsAs//6vDO2+mRm3foJR6bNIN0urIfjq64PB43pj412HZwzz/fO2y5g3iYKFaeiIhIa5inqxZ/d9TAqSaSNWpHCmgeawZSZj3gEF53dSFEptp5GGN5/K8pj1VUA5tEyVp/L44BIMxI+PhOo/z3AyY4930vHupeV/yw7HO1YwEAhBCYMfUJ+6E92zY5bLlDuDhm5U0IIYTbcb/rzJEN579/18FvwRujsYTDWLeVApJGqJ2FMfb/uAWZlYsQAx1Y0NdY7456ftP9uFiEEPhqjwePrnLAEBrlffK1uXKdxCaq5flu8SfeBZ+8cdRhy20qhMhVLQir9IjIRDrjBkty98TwLg/4z1csAchxah/OLpmUKlz2GP7Qy8rK1q1bq2o0mtkAGoEbSAFAAbDb4/E82KJFi7NXruQCmZU5ImoWYaJ1Z562mKQAHfLT6hJ4+Xen8uFml9SgZSdl/KuzJaPJUq4Z9vzzF6aOH5LtdNibCiGOluvJGSsEEVUhreGf0Pb3RQe36uP/Xw/5KSEEUmY/YvVknBoghPhB7TysYtqxY8eKatWqNYiMjMyWJKnSF3+KotC5c+dC0tLS9iYnJ/e+cj1/gmBlzqzFI4+01OoCtTgGAIuO8FY3g7TzEQtC0v7Ewz3riyX/e6/czn/+bAqmPTfC7nTY+3NxzPyFECJduB0dMtfOszpO7FQ7TsAiIoS0ucci6c3/UTsLq9AacXH8/yRJEpGRkVnIa1G/en0552GVDBEZvAKDH2imqxB38sSHS/htuFla2FdHvy98UzxyZ6J39z9/lek53S4npj41JNflckwVQvxUpidjrISEEMeFxznw3Dev23n4t9IzNegAIZRWRJSgdhZWYUlcHF8u//UotBbmApmVtbubV5eVmqEV6612Rz0t/h1noUcb2+j1J/pi4kN3KtmZ6T4/jxACM6c97TiXevJ3t8v5us9PwJgPCCFWC4/rw3PLp9m4C23pSFo9LE1ul0mje1jtLIyVFSJq0adPn9oXf3a73QgLC0vu3LlzPABMnz69ChG1WL58edDFbebOnRtKRC0+//zzMABwOp00ZsyY6Jo1azZKSEho2Lhx4wZfffVVsK+zVqyqhfmdUAPGPdZaF3T9LQOPQUOY2NEgHXjUgrrO7eLRuxrj8+kvQVF8VyCs+X6x2Pj7qjS7zXqv4BsGmB8THud/XGeOHMjesMSjdpZAZWlymxYkDScivjazMhdZrUYyEbXw1SOyWo3k653TaDQqBw4cMFqtVgKAb775JjgqKspdcJuEhAT7ggULwi/+vHjx4vD69etfGnj9ySefrJGWlqbdv3//nkOHDu1ZtWrVoezsbJ/fA1EhvvZm/omIapm1SO6TWLHfZrEhEpYPMsm/H/Ng5IpPxEPfzxMPTpghtenQ44aOezb1BD599wWH027rLYTI8VFcxsqEEMJDRL2zNny1Rx/XONgQk6R2pICji6wF2Rym82Smtgfwh9p5WMV2/kyqpuZz3/nseMffuLNYF/tbb701a8mSJaEjRoy4sHDhwvB77rkn46+//rp013ubNm2sf//9t8XpdJLD4aBjx47pGzZsaAOAnJwcacGCBZH//vvvTqPRKAAgNjbW8+CDD17w2RPJx59SWZnRy3hwWLKWDJrAvTmvJDrV0uDgWDO92M5DMyaOwLPDOnrPnTldqmMpioJ3Xnwo1+vxTBFC7PJxVMbKhBDilPA4h57/9nWb4rSpHScgWZp2N5POOErtHIyVlaFDh2YsXrw4zGaz0b59+0zt2rW7bMhSIkKHDh2yv/766+AFCxaEdu/ePfPiur179+qrV6/uCg8PL/O+XFwgszJBRLJGwsMPtdDp1c5SnjQS4Ym2ejr6uAXtjP/iyf4tMePVccLjKdm3zisXfew9dezQQY/b9VYZRWWsTAghVihux7cZv3zsUDtLIDI36CgJr+duIjKonYWxstCmTRv7qVOn9J9++mn4bbfdllXYNkOGDMlYtGhR+JIlS8KHDx+eUd4ZAS6QWdnpHBMs6ZIDYOa8shBpljD3bqP8+3ATzm5aIkZ3r6usWbW4WPue+Hc/Fn36ptNus/YTQnjLOCpjPidc9kdsB9Zn245sVjtKwNEER0AXVdcL4E61szBWVrp37545efLk2GHDhhVa/Hbu3Nl24MABY0ZGhqZJkybOi8uTkpKcqampugsXLpR5/coFMisTFh2GjmymNaudQ22tomXseNgsvXMr0dw3n8CTA1t7Tx47WOT2brcLb73wYK7H7X5SCPFvOUZlzGeEENnC7RiQ/t07Nq+t0AYidg1BTbsHSQYLj2bBKqxHHnnk/Pjx41Nat25tL2qbKVOmnHrllVcu66cYFBSkDBo06PyoUaPiHA4HAcDx48e1H330UXjhRyk9LpCZzxGR7FXQp1+Slt9fACQijGymo+NPWHBHVCqeH9YRb/1npHA5rv4GeukX77szzp/Z5PV6PlUhKmM+I4T4Q3jdn6X/+AF3Ri4hU72bINyuW4ioitpZGCsLdevWdU+cOPGq6Z0LGjBgQHavXr2uukH9/fffPx0REeGpV69ew4SEhIa9evWqGxUV5fPRc3iqaeZzRNQxPlxaeegxS4Uc3u1G7TnrxQMrHMq+DKJ+D02mOwfm3Y+TevIonhzaxeZy2usLIU6pHJOxG0ZERtLqT1YdMKWKIaah2nECytmvX821H9r4jBDKTLWzsIphx44dx5KTk89f/DmyWo3k82dSfTbMVERUdc+5tJQdvjpeedmxY0dEcnJyrSuXV+zxt5gqzFrcO7SJ1qR2Dn/VsKqMDQ+YpGX7PHjkk0n4ceEH3nGvzZHnfvhKrqJ4p3JxzCoKIYSdiMZlrP5wVvWRMyw8vG/xWZrcZnae3PUQAC6QWZkIxGK2PPFfK+ZTRCQJYED/JE3lvDuvmIgI/ZK0OP6EBcMTMumlh3rg4O6tVo/b9Y7a2RjzsYWe7HMnbPvWqp0joBhrNYPidjbgbhaMqYMLZOZrrSNMpGkQyfVxcZi0hMkd9VKIgexul3OMEMJ9/b0YCxxCCEW47A9l/PKJTXE7r78DAwCQRgdDbEMngBubcYgxVipcIDOfMmow6L7GWqPaOQLJ+3+7vDa3+EsI8bXaWRgrC0KIdcLr+SN78zc8DXUJmBLbB0l680C1czBWGXGBzHyGiEiWcG+/JC33bS+ms7kKXvnD6c52YozaWRgrS8Jleyx74xK3N9fnM8JWWMa6raB4XLcSkVbtLIxVNlwgM19KNmvJ3LQav62Ka8JvTgeAL4QQRQ+OzFgFIIQ4AmD2hT/m8Ax7xaSxhEMTGuUGcIvaWRirbLiSYT4jE3rek6TREpHaUQLC3nNefLnT7cl1Y4LaWRgrD8LtnGzb96fbde6Y2lEChjmxg5k0+r5q52DMV06cOKG5884768TGxjaqW7duw44dO8bv3LlTT0QtXn311aoXtxs2bFjc9OnTqwDAPffcUys6OrpxYmJiUlJSUoNffvmlzCci4wKZ+UyIge7uXlejUztHoHj5D6fNo+B1IYQq88wzVt6EEBeE1/1ixs+zrGpnCRTG+NYyJOketXOwiqd6jehkImrhq0f1GtHJ1zunoijo3bt3fIcOHXJOnjy5+8iRI3tef/310ykpKdrw8HDPxx9/XPXiDHlXmjp16qn9+/fvnTp16ukxY8bU9P0rcjnuK8p8goiMOhlNOtbit1RxnMpWsOKAh9wKj3HKKhmhzHSlHX7W/u9Wi7FOC7XT+D1dVF2QJIcQUX0hxAG187CKIy01RfP1hjSfHa9vu2rXLQC+++67II1GI5599tlzF5fddNNN9gMHDujCw8M9rVq1sn744YdVxo8ff76oY3Tv3j1nyJAhel/lLgq3IDNfubl+FckRrOfuFcXx/kaXSybMEULwHUusUhFCuIXb8VTmn/OumkKWXY2IYExoJ4Gol9pZGLtRO3fuNCYnJxc5/fykSZNSZ8yYEeXxFD3gzaJFi0ITEhLsZRKwAC6QmU8YNLi9d31NmfcJqghynAKztriUXDfeVDsLYyr5xp1+0uM6e1TtHAHBWKeFQdJbequdg7GylpiY6GratGnuxx9/HH7luhdffDEmMTExafbs2RGfffbZsbLOwgUy8wmDBr271eXZ84rjs20uRSPhVyEEVwesUhJCuIXX+9/szd+WeStQRWCIbQjF7WhJRPw3lgW0xo0b23fs2GG61jaTJk1Ke//996srinLZ8ot9kP/6669DrVq1KvPRcLhAZjeMiMLsbtRqG8N/u6/Howi8vs5lz3LiFbWzMKYqxTPTtv9P8tq5p8X1yOYwyMZgL4BGamdh7Eb06tUrx+Vy0TvvvBNxcdkff/xhOnz48KUb/Js1a+ZISEiw//rrryHqpMzDBTLzhc6to2WHTub+x9fzzT4PnB5xWAjxt9pZGFOTEOIsJM331p0/K9ffmhlqJcsA2qudg7EbIUkSVqxYceTXX38Njo2NbRQfH99w8uTJNeLi4twFt5s4cWLqmTNnVB0Vi4ccYDfMokPP3vU1QWrn8HdCCEz5w2nNcuIltbMw5g+Ey/Zm9uavuwe3ustMEn8DdS2GmslG++FNPQHMUDsLqxiqVa/hKc7IEyU5XnG2q1WrlnvVqlX/Xrn80KFDey7+u127dnZFUbZe/HnZsmXHfBKyBLhAZjdMJtzepbaGm4+vY/1JL45lKTkAVqqdhTF/IITYJBnMJ+3/bkk0xbdRO45fM8Q2gvC4byIiEkIItfOwwJeacnqH2hn8GXexYDeEiIJtbkQ1rspvpet59U9nrs2NV4UQXrWzMOYvhNP2Wvbfy7gj8nXIwVVBGp0WQLzaWRirDLiqYTeqeWKEZNNy/+NrOnpBwR/HvEIR+FztLIz5ma9caYfhTj+ldg6/RkQwxDVWwP2QGSsXXCCzG0JAq/Y1ZaPaOfzdV3vcikbCUiFEkQOkM1YZCSGcAD7K3rLcqXYWf2eo1dRCevPtaudgrDLgApndkDAjdWkXI6t6p2kgmLfTbc1xYYHaORjzR8LjmpG7+zehOPnz47XoY5IAIbgFmbFywAUyuyEur2jRqgbffX4tJ7MUHLmgyAB+VzsLY/5ICHGKNNpt9mPb1I7i17RVYiG8rggiClY7C2MVHRfIrNSIKMKjICShCr+NrmXZPrfQyfhOCOG+/taMVU6Kw7rAdmA9NyFfA0kytGE1bACS1c7CWGkQUYtRo0bFXPx50qRJUU899VQNAHjqqadqTJo0KUq9dJfjYd7YjWjZuKpkl4i4i8U1zNvpzsl2Yr7aORjzc9/Zj2x+Syhe8JjIRdPXqK9znz/RDMCfamdhgS2mWkTy6TPpPqsDo6OqeE6lnb/m0HE6nU6sWrUqLDU1Na169erFGjdZLVwgs1LTSGjTsabGrHYOf5ZmVbDnrKIF8LPaWRjzZ0KIY5LBfMaVerCmPrqB2nH8lq56fSMdWH8zgOlqZ2GB7fSZdI2Y7LveOvTy9YttWZbFsGHDzr322mtRH3zwwWmfnbwM8HfjrNRC9NSpTYzMH7Ku4dv9Hhi1+Dn/Tn3G2DUIj/sr26GNft2qpDZd1dogUEu1czBWWs8888zZr7/+Ojw9Pd2vvyriApmVmssrGjaJ4rfQtczd4c7OdGCu2jkYCwhe9ze2/evsasfwZ9qImlDcjlgi8uvigrGihIeHK/3790+fNm1aVbWzXAtXN6xUiEhv9yC8dii/hYqSblOwNdWrB/Cj2lkYCxCbvLkX4Mk6q3YOvyXpDJAMQU4AddXOwlhp/ec//zmzYMGCiNzcXL8tIvw2GPN7daPMxDPoXcOKAx6YtFgrhMhVOwtjgUAI4YWk+cF2ZJNQO4s/00XWVAA0UjsHY6UVFRXl7dWr14UFCxZEqJ2lKFwgs9KqVz9C4ovYNXy5y52T6cActXMwFkiEy7bEtm9tjto5/Jmuej0zSGqidg7GbsSECRPSMjMz/fY+Jr8NxvwbAfWbRPEU00URQmDDKa8OwC9qZ2EswPzkTD1kUJw2SHqT2ln8krZKrCzpTc3VzsECW3RUFU9xRp4oyfGut43NZrs0G1BsbKzHbrdf+vndd99N8VUWX+ACuQTi4uJ+s1gscWrn8AWr1XrixIkTXUq7f4gBTRtESFpfZqpIjlwQICBXCHFG7SyMBRIhRLZsDNrmOL69janeTWrH8Uua0CgAVEftHCywXW/M4sqOC+QSsFgscXv37q0QN0YkJSXd0P4yUcP6PINekbakeKHX0D9q52AsECkO6xL7v1ubmurdpFc7iz/SBEdBeF0x19+SMVZaXOGwUrG5Ra16XCAXaeMprzvTIdaonYOxALXZeXqfQ+0Q/kq2hEF43BYiMqidhbGKiiscVmJEFKoIGKpZeASLoqw97rEpApvUzsFYgNrmzkgxCS/PGVIYkmRIphA7gArR5Y8xf8QF8g0gIowfP/7Sz2+//TZeeuklAMBLL72Et99++6p91q5di+bNm0Oj0WDp0qVXrc/OzkZ0dDTGjh1b6DmdTicGDhyI+Ph4tGnTBseOHQMAHDhwAC1atEBycjI2bNgAAPB4PLjttttgs9lu8JleJSEuRLITcYFcGK8isPecYgKwRe0sjAUiIUQOaXRn3ekn1I7itzTBVb0Aaqmdg7GKigvkG6DX6/H111/j/Pnzxd4nLi4OX3zxBQYPHlzo+okTJ6Jjx45F7v/ZZ58hLCwMhw8fxpNPPonnnnsOAPDxxx9j2rRpWLp06aXCfObMmRg6dChMJp/fCR5bO5SL46LsO69Ar8F5IUSm2lkYC1REtMmVdljtGH5LG15DCy6QGSszXCDfAI1Gg9GjR+O9994r9j61atVCkyZNIElXv/Rbt27FmTNn0K1btyL3X758OYYPHw4A6NevH3799VcIIaDVamG322Gz2aDVapGZmYmVK1di2LBhJX9i1xddM1TSlcWBK4ItKV7IxK3HjN0IxZm71nn6APdDLoImPNoISeaRLFjAaN26df1ly5YFF1w2ZcqUqkTUIjExMeniIyEhoSERtfjnn39U7WPPo1jcoEcffRRNmjTBs88+e0PHURQF48ePx7x58/Drr78Wud3p06cRGxsLIK9ADwkJQXp6Oh599FEMGzYMTqcTH3/8MaZMmYIJEybget0giCgYgEsIUewLkVZCTM0QiW8OKcJfJ73OCw7wDXqM3ZjtztSDTgD8t6YQmpAoknSmhmrnYIErpka15NOpZ3w3DnL1KM+plLQih47r379/+sKFC8Pvueee7IvLli1bFv7DDz8c6N69u/XisrFjx0YnJSXpmjdvruoHZC6Qb1BwcDCGDRuG6dOnw2gs/bwZH330EXr27Hmp+C2KEFdPXkdEiIuLw++//w4AOHz4MFJSUpCYmIihQ4fC5XLhlVdeQb169Qo75DcA2hNRLoC0Kx5nrvg5RQhx1qJD3ehg7mJRlHUnvE4Am9XOwViA2+u5kGIQQlz3g35lpAmpCkBUiGFHmTpOp57RiJ+f99nxqOu0a9aUQ4cOvfDaa69F2+12MhqN4sCBA7qzZ89qu3Xrdqk4/uGHHywrVqwI2759+16fBSslLpB94IknnkDz5s0xYsSIUh9jw4YN+PPPP/HRRx/BarXC5XLBYrFg2rRpl20XExODkydPIiYmBh6PB1lZWQgPD79smwkTJmDq1KmYPn06hgwZglq1auHll1/Gl19+edV5hRC3Ut7VJwxAtUIeSfn/jQJQhYhuqWKkuOoW7p1TGJdX4HCGYgKw7bobM8au5RwgPIotUy+bw9TO4ndkcxiE4o1QOwdjxVWtWjVvcnJy7rJly0Luu+++zDlz5oT37t37wsUup+fPn5dHjx5d67PPPjsaHh6uqByXC2RfCA8Px4ABA/DZZ59h5MiRpTpGweL1iy++wJYtW64qjgGgd+/emDNnDtq1a4elS5eiS5cul7Wu/PHHH4iOjkZCQgJsNhskSYIsy9ccyULkNUtn5D+u+6ktzEhVo3iIt0IdOK/ApMWZTIfIVTsLY4FMCCFkY/C/7vMnG3OBfDXJEATh9VjUzsFYSQwYMCBj8eLFYffdd1/m119/HT579uxjF9eNGDEirl+/fhndunXzi+snNwP6yPjx44s1msXmzZsRExODJUuW4KGHHkLDhtfvQjZp0iSsWLECAPDAAw8gPT0d8fHxePfddy8rooUQmDp1KiZOnAgAGD16NJ5//nncc889ePrpp0v5zK7m8iK0qpkL5MKcyFKgkYjHpmLMB4Ti3c5DvRVO0psAr8dARLLaWRgrriFDhmSuX78+eN26dSaHwyHdcsstNgD44IMPqpw8eVL/5ptvpqid8SJuQb4BVuulbjOIioq6rJX24njIV2rVqhVOnTp1zePef//9uP/++y/9PGXKlEv/NhgMWLJkSaH7ERF+/vnnSz83aNAA//zj29mOiUiSCEERJi6QC3MqW8CjiCNq52CsIhAu2z+us0cHAOApp69AkgzS6FzC4wwFkK52HsaKIyQkRGnbtm3Ogw8+WKtv374ZALB3717d1KlTo9esWbNfq9WqHfESbkFmJRWql+HWyVwgF+ZElqLkuMCDtzLmGyc92eedaofwV6Q3uQGEX3dDxvzIoEGDMg4cOGAcOnRoBgBMnTq1ut1ul/r27RtfcLi3H3/8UdUuRNyCzEoqItRALnCLTqEOX1DsigB/J8yYb1xQ7FlXD93DAACSwexVci9wB21WKtHVozzXG3mipMcrznbDhg3LHDZs2NaLPy9YsOA4gOO+yuErXCCzkjKbtFD97lJ/dSxT8QA4qXYOxiqIDMVu5a+riiAbguHhFmRWStcas5hxgVwiVqv1RFJSktoxfMJqtZa2ldNk1PL1qigpOUICkKp2DsYqiAzFmcs3oRVBMgVL4AKZsTLBBXIJnDhxoovaGfyAycwFcpEyHUIL4PrDmTDGiiNDcdm5O1cRZFOIFlwgM1Ym+CY9VlJGs07tCP5JEQI2N/TIG0+aMXbjciEUEh632jn8kmQK0YMLZMbKBBfIrKRMZi3x+6YQWQ5AK8EhhOCrOWM+IIQQJGtzFYf1+htXQqTREUgyqJ2DsYqICx1WUiaLjt83hUm3C+hk5Kidg7EKRZZzvA7+tSoMSTIgSf4zcCxjFQgXOqykjBYdz9xUmHSbAq2ETLVzMFaREMmZChfIhSMJRFwgs8BiMpmaHThwQGcwGJonJiYm1a1bt+HgwYPjvF4vDhw4oCOiFuPGjatxcfvU1FSNRqNpPmzYsLjyzMk36bGSMnGBXDibGyAi2/W3ZIwVG1GGYucuFoUhSQaIuEBmpRJZPTL5fNp5n9WBEdUiPOdSzxV76LjY2Fjn/v3797rdbrRr167+/PnzQ9u2bWuLiYlx/vTTT6EAUgBg7ty5YfHx8Q5f5SwuLpBZSZksOvAf5EJoJEDw7xRjviWUc9yCXASSAHCBzErnfNp5TaMvGvnseLvv312q659Wq0Xr1q2thw4d0rdt29ZmMBhEfHy8fe3ataYOHTrYli1bFt6nT5+MlJSUch0igLtYsBKRCQaeZrpw2rx2dS6QGfMh4fWcVRy5asfwS3ktyNzFggW2nJwcae3atcFNmjSxX1w2aNCgjPnz54cfOXJEK8uyqFGjRrnf/M4Xc1YiXgG3l+fRK5RGIggh+GLFmC8R6UnmXl2FkiSA+DrOAtPJkyf1iYmJSUSEHj16ZA4YMCD7wIEDOgC45557sqdMmRIdFRXlvueee1QZOpV/sVhJud2KEAC4GfkK2rwuFnwlZ8yHSJKDScsjmRWKZBC4BZkFpot9kAtbZzAYRJMmTWwzZ86stnv37t1fffVVaDnH4wKZlZjH5YUCLgSvopEAIfh3ijHfIgtpeDK9wuR1seB7QljF9Nxzz6V16NAhp1q1al41zs8Xc1ZSbrfCBXJh+CY9xsoAkYW0XCAXigjgv8UsgLjdbuh0OlGcbVu2bOlo2bJluY9ecRFfzFlJedxeUaw3d2WjlQlC8MWKMd8SZom7WBRKeFwQQvAdjKxUIqpFeEo78kRRx7veNlu2bDHGxsY669ev7zp06NCeK9cXtfzxxx9PB5Duo6jFwgUyK6mLXSzYFTQSoHAXC8Z8SwgTtyAXTridgOLNVjsHC0wlGbPYF958883Ijz/+uOpbb711sjzPW1p8MWcl5XZzgVwovkmPMd8TXCAXSbgdEF53lto5GCuOZ5999tyzzz57Tu0cxcXjILOS8ri4i0Wh8luQuUBmzJeEYuBRLAqnuB0KFC9PM8hYGeACmZWU262AC+RCaGXiApkxX1O8BolbkAslXHYPAO6DzFgZ4AKZlZQtxyW4i0UhgvWAIqAlIqPaWRirKITi1fEwb4VT8gpkm9o5GKuIuEBmJZVx3sZdLAojESHSRDYANdXOwlhFQEQExavlPsiFEy67Am5BZqxMcIHMSio93SZ4Fr0i1AqVFAC11c7BWAURDFnjIYl7LhVGySuQuQWZBQxZllskJiYm1a9fPykpKanBzz//bC64/uWXX66q1+ubp6enq/5Lz6NYsJLKyHQIft8UoX6EpNtwyssFMmO+kaAJirQDPFtcYYTbIcAFMiul6tHVktNSzvjsel6tRpQn9XTaNYeO0+v1ysXppZctWxb8wgsvxHTt2vXAxfVLly6t0qhRo9wvv/wyNH/sY9VwocNKKsPqgk7tEP6qXrhk1MuIVzsHYxVEfW1ELH9jVQTFmQsAOWrnYIEpLeWMZtbRN312vIdrP1uimjIrK0sOCQm5NLnInj179DabTZo2bdrJ119/vToXyCzQ2BQB2N0CRi1ft65UO0yCWUdJaudgrEKQ5Aa6yNrm629YOXltWTKAVLVzMFZcTqdTSkxMTHI6nXT+/HntqlWrDl5cN2fOnPC+fftmdO/e3Tp69GjD6dOnNdHR0dedna+scB9kViJCCKHXIDfDzvfpFaZ2KEEIUVftHIxVBJLO1FxTJYavU4UQQkBxWA0A0tTOwlhxXexicfTo0T3ffPPNoREjRtRWlLyBsb755pvwYcOGZciyjB49elyYO3dumJpZuQWZlZhORla6XYREB6udxP/UDpNg96C62jkYqxCEkqgNj1E7hV8SzlyAyCOE4D7ILCDddtttuRcuXNCkpqZqTp06pT1+/Li+e/fu9QDA7XZTbGys8z//+Y9qM+/xJ3NWYjLRBW5BLlykiaAI6IgoSO0sjAUyIpIUtyNGGx6tdhS/5LVeAGl0GWrnYKy0tm3bZlAUBVFRUZ65c+eGjx8/PuX06dO7Tp8+vevs2bM709LSdAcPHlTtnicukFlpnE+3cYFcGCJCNQvZwUO9MXajYkhrcEt6k9o5/JInNwMkyWfVzsFYSVzsg5yYmJg0aNCgOjNnzjym0Wjw7bffhg8YMCCz4LY9evS4MGfOnHCVonIXC1ZyTq84eiqbJ9MrSp0wSZzI8tYGsFPtLIwFsPrasBputUP4KyX3AiBwWu0cLHBVqxHlKenIE9c73vW28Xq9Wwtbfvr06V1XLps9e/YpX+QqLS6QWYlZXdh/KENxATzcW2GSIiXjH8e89dTOwViAq6+NrMVT6BXBa82A8LiOq52DBa7rjVlc2XEXC1YaRw+mKw61Q/irW2I1ujAj3a52DsYCGWkNTXSRNQ1q5/BXnpx0j/A4uUBmrIxwgcxK49iRC9zFoijta8qwuUVbIuLfL8ZKiTTaZL5Br2je7HMO8BjIjJUZvoCz0jh6OlsYhOAb9QoTEywhRE8CQAO1szAWiIhIEi5nQ21Vvte1KO7MVC+Ak2rnYKyi4gKZlZgQ4gIALw/1VrQutWUJQHu1czAWoBpKBovQBEWoncNveTLTdAAOqJ2DsYqKC2RWKkYt0o5lcoFclK51NKYwA3qonYOxANXJULuprHYIf+V1WCE8bgk8ix5jZYZHsWClIhMdPZqp1G5Rg69hhWlfUwOXF+2JiAT3RWGsRCSD5U5DrWZGtXP4K0/6KUg6wwmvPYf/trCAkZaWJnfq1Kk+AJw/f14rSZIIDw/3AMDPP/98cMyYMbHbtm2zhISEeLRarXjqqafSfv755+DNmzdb3G43nT59Wl+rVi0HADz33HOpI0aMuFCWeblAZqVic4u9Ry8oXdTO4a/qhhG0MgxwozaAf9XOw1igICIije4mQ2wjtaP4LXfGaQDYq3YOFtiiIyOTU86f91kdWCMiwnP63Lkih46rVq2ad//+/XsB4KmnnqphsVi8U6ZMOaMoCpo3b544ePDg9JUrVx4FgIMHD+qWLFkSOm/evBMAcODAAd2dd96ZcHH/8sAFMisVuwcH959XHAB4GKZCEBE61tR4lx/wdAAXyIyVREtJCHPm+oUIatoD+uoJaufxO+70k17FkfuP2jlYYEs5f16zt36iz46XdGB/qWrKlStXBmm1WvHss8+eu7isXr16rgkTJqg6UyQXyKy0dm9J9TrBBXKRutXVWH4/5rkdwBdqZ2EsgLSPrZKgmC5k4vCC/8gkaQSFVlV0dVvJQc16QhNURe18qnOdPZoLiP1q52DMF3bt2mVs0qSJTe0cV+ICmZXWjgPnFaMiBCQitbP4pQ41ZSgCndTOwVggMemDBnRtOlBOrn0LFMWLk+mHaf/JLdKOw+u9p/9eJuv0Fq+IiCVjgw6SpVEXSNrKN9meO/0kwCNYsApq6NChcZs2bbJotVqxe/fufWrl4AKZlYoQIsOiI+vRCyK8bjgXyIVJipSgCIQRUbQQ4rTaeRjzd0RkliVNs3rRzQAAkiSjZmR91IysT7c3HyK73A4cSdst7zm5Sdm98Rsl9ZdPJK051Isa9WRL49tgqN0CklSxB2cSihdea7oJwCG1szDmC40bN7YvX7487OLP8+bNO5Gamqpp2bKlqnMJVOy/JKxMGTS0e8cZr9ox/JZEhNvrajwAequdhbEA0Sm6Sh2HUWcudKVOa0CD2Jbod9MY6aV750mv3LcQA1uPlpO8eq9t5Xs4894AnP3kYW/6TzPhOlcxZ2H2ZJ8DydosIYTffSXNWGn06tUrx+l00htvvBF5cZnValW9PuUWZFZq2U6xflua95a+DbSqv5H91bBkrXnNMc8oADPVzsKYvzNoTfe3SrgtqLjbBxnD0DK+C1rGd5GFEDibdQr7T22Vdx5b7/13zlOyrNEJhFUT+oQ2kqVpD2hMIWUZv1y40g6DZO1utXMw5iuSJGHlypVHHn300djp06dXCw8P95hMJu9LL710Ss1cXCCzUnMr2Pb3Ka8VQLDaWfxV93gNnF4kEVENIUSK2nkY81dEZNLI2jta1O1cqj5bRISo0FhEhcaiY6M+slfx4vjZ/bT/1BaxY+96JW39IklnCPaKyDjJ3LATmRp0gKTR+fpplDlnygG34sz9Ve0cLPDViIjwlHbkiaKOV9xt33333cuuhzVr1nR/9913RY74VL9+fdehQ4f23Ei+kuICmd2IHdvTFJ4p5Br0GsJd9TXexXs8AwC8r3YexvxYr5qR9T3BpnCfHEyWZNSp1hB1qjWUerYcDqfbjkMpO+Q9Jzd5d69dIKWu/oi05jAvxSTKlibdYKyZ7JPzljXnyd02COVvtXOwwHetMYsZF8jsxhzJdAhtpkMg1MA36hVlaBOt6cfDngfBBTJjRTLqLA+1S+xZ7O4VJaXXGtGoZls0qtlWHnjL48jMPY+Dp7fJO4//5T3w9etyplCEFByhaGs3ly3NekAXHl1WUUpNKF64zh0zAtiidhbGKjoukFmpCSG8VYx0ZOcZb4MONfmtdCWPouCDv92YtU3xuqBJIqK6QogjaudizN8QURWNrL0pufYt5XbOUHMEWtfritb1uspCCKRdOE77Tm2Rdh5b7z227TFZozUoCK8BQ712UlDy7ZAMlnLLVhR3+imQrMlQPK4MtbMwVtFxVcNuiEvBX5tOexM71NRwE3K+5fvdmPaXW9l1jqRqYWbxUJ9m0vbDZzxL/zxwP4CJaudjzA/1axDTymPUmVUZ1JiIUD28FqqH16IuTfrJHq8bx87uk/ae3Cx27vhNSVk7T9IZQ7yIqi2ZG3YhY+JNkKTyv3y60g6BJHlTuZ+YsUqIC2R2Q6wu/PLTEU//p2/SV+ob9XakeTDpd5dYd1oSIEka3q0ZPrm9MRrXrkoAsO1wmnb5hkMPEdFkIYSidl7G/IlRZ3m4XWL3wsd2U4FG1iK+ehPEV29CvVs/QDanFYdStst7Tvzt3fPrZ1LWqvdJYwn3SLENNZam3WGILp/hWp2n9jkUh3VNuZyMsUqOC2R2o/7866RXXxln1DtrVTD5DydWHCbvBZsi33VTgrLkgWS5Y5M4yPLlsxU0rRuFauFmw5GUzE4AflMnMWP+h4hq6TSGxKTY1mpHKZJJb0Fy7VuQXPsWGQAycs7gwOl/NDuOrfceWjxJBkmCgiMVXd2WedNhh1QtkxyOU7tdALgFmbFywAUyuyFCiNPBesrce06JalS14g9o4fIoeGejG//boXhPZSly68Qa3nfGNJN7t0uAyaAt8gUgIjzau4Xlpbl/jgUXyIxdotXoH2uX2IM0slbtKMUWHhSFdok90C6xh6wIBSnp/9K+U1ukHcfWe09uXi7r9CZFVIkmY+ItZGl8GySd6YbPKTwueDLTTAC23/DBGFOJLMstEhIS7B6Ph2RZFvfee2/6xIkTz8iyjO+++y7o3nvvrRsdHe26uP20adNO9unTJ0eNrFwgsxsmEdasPe4dVFELZEVRsGSvB29v9Hj3noccGxmsPNq/mXRv5yREhZmL/aRH3N6YJnz+Rw8iihVCnCzLzIwFAiIyaWXd6E6N+qrS99gXJJIQExGPmIh46tp0kOz2uPDvmT3S3hN/Kzs3rxIpv30u6UwhXlSLl8yNbyVjfJtSTYftOnMEpDWcUBxWnkGP+URM9erJp9PSfFYHRler5jmVmnrNoeP0er2yf//+vQBw+vRpTf/+/etkZWXJ7733XgoAtGzZ0rpmzZrDvsp0I7hAZjcsy4nVPx/x3DmmlU7927x9aPPpvH7FG1MlaDUyjezekuZ1a4zEuCqlmjkw1GLAqJ5NpdmrdjwL4DEfx2UsANGQOtUaITKkhtpBfEar0aF+dDPUj24m3d3uYVgdWTh4eru8+8RG794fP5Sy3G+TxlLFI9dsrLE07QF9tfhiHddxfKcCr2dVGcdnlcjptDSNmPWRz45HD48pUU0ZHR3tmT179rGbbrop6Z133vG7ibS4QGa+sPaP4x5JCAEK8H7Ip7IVTF7jwKqjsjfbocj3tK+vrBjTVL65YQwkiW54Su2n+7XWffL99geIaKIQItMHkRkLSEREBq3phduSB1aoD9ZXshhC0LxuRzSv21EGgPPZKdh/aqtmx7H13iNfPidLklYgNErRJbSWg5J7QBNU+EQptiObrMLjXF2u4RkrY0lJSS5FUXD69GkNAGzZssWSmJiYdHH9smXLjjRs2NCpRjYukJkvHHV64fr3gjDVDQ+8AtnmUvDmXy7M2w3v6Wyv3L5xjHfGuGbyHW3iYdBpfNpvJLZqMHq3ixffrj/0KIBXfXlsxgJMd4sxNCIxpoXaOcpVRHAN3JJUA7ck9ZIVxYuT5w/RvlNbpR0H13lTNiyRdQaLV1SJI1NSB8ncqAskjQ7C44LrzBEDgLVq52fM14QQl/7NXSxYhSKEEOFGWrf2uOfOuuE6teMUi6IomLfTg/c3eb0H0oVct0aoMn5wM2lgpyRUCTaWaWfqF4fcZPpu4+GniegdIYSjLM/FmL8y6sxTerYYZgn0b51uhCTJqFk1ETWrJlL35kNkl9uBw2m75D0n/lZ2/7VMSf3lY0lrCvMqYVEyafRHFY87W+3MjPnS3r17dbIsIzo62rNjh3/NfM0FMvOJCw6sWn3E02VEMx/crl2G1p3w4KU/XMqWNCKjXocHezanJV0bIz467Ia7TxRX49pV0ap+dc3aXSfvAzC7vM7LmL8gotYWQ0hSi7qd1Y7iV3RaA5JiWyEptpXU/+axyLZl4GDKdvmHrfOUs24HTy/NKpSUlBTNqFGjao4YMeJsaW5cLWtcIDNf+fGHw553/HE85KMXFExa48BPxyXF5hbSwE5J4pVxyVLbBjVAKjVfTR56i+WuScsmE9H/eOIQVtkYtKbXbm82xCDLfAm6lmBTOFrGd8GqLXOsQvF+rHYexm6U0+mUEhMTky4O8zZw4MD0yZMnn7m4/so+yM8991zqiBEjLqiRlf86MZ8QQhwNNdC5Tae9cW1j1H9bWV0KXv3ThUV74U3N8cq3Nqvp/XR8M7l7qzrQaWXVx6PrlByHmMig0H0n0vsA+FrtPIyVFyJqbzGEtrslqZf/NRn5oWxbBjJyzmgBbFQ7C6tYoqtV85R05InrHe9623i93q1FrbvzzjtzcnJytvsqz41Sv5JhFYbTi69WHPA80TZGo8r7SlEUzN7mxowtivdwhpAT46ooE4Y3l/t1qI9Qi0H1orggIsI7D3WxDJj67XQi+k4I4br+XowFtvyRKz68u91DRq0mMO5XUNu+U1ug0+r/dDtcbrWzsIrlemMWV3ZcIDOfcXjwzeI97odeu9UQVJ7n/eWIG1PXucU/ZwghZgNG39GUht7WELWqhfp1C1X3VnXQLL5a6Ma9p8cAeF/tPIyVg75BprDarRJu869+WH5s59H1uTandYnaORirbLhAZr70d0qOoOOZCmqWcW164LwXE9c4xe8nJeH0Qhpya2Pl7aebyC3qVVOtX3FJERE+eqybufVjc14horlCiAy1MzFWVohIq9cY/9v/5scsPhhSvFJQFC/2n9oiA+DxjxkrZ1wgM58RQnhDDPTjdwc9/R5t7fuvTzMdCl5Z68KS/fCey/XKt7eso3wxuKnctUUtaDXq9ysujUa1I3Fv5yTN4t/3vQrgEbXzMFZWiKRRNarUCW0Q01LtKAHjxLmDIEk+y1PTM1b+uEBmPpXtxOJFe9zdHm2tC/bF8TyKgo82uTFrm+I9ekGRk+tW9b7yYHP57pvrIdisD8ii+Eqvj+xoWPz7vuFE9J4Q4qDaeRjzNSKyaDX61/rfPNYcIF/w+IWtR9a4PV7XYrVzMFYZcYHMfO2nzae9hhynQJC+9BfC7w+68do6t7LzHEmRoSbxUK+m0pBbGyImMrhCFMUFVQ0z48UhN+mmLdr4EYDb1M7DmK9pZN1/Gsa20cZF1lM7SsBQFC/+PviT2+N1z1E7C2OVEXcEYz4lhMg2afHPz/9ed7SXq+w+48Hdi3NFxDsOZehKBa1aNhXr3r8P/857hJ4b1I5iIn3SKO2XnujbSjbqNO2IqIvaWRjzJSKqL5H0ZN92D/v1JEL+5nDaLgihpAkh9qidhTFfOnLkiPbWW2+tW7NmzUaxsbGNRowYEetwOOivv/4yLl68OOTidk899VSNSZMmRamVk1uQmc9lOjB3wS53474NtObrbXvepuDl35345hB5M2yKfGfbeGXh/U3lLk1rQpalCtdaXBSDToPpj3Y1jXxn1WdElCiEcKqdibEblT+s2/yeLYfrwoNUu84FpL8PrLY73XaeHISVmepRNZLTzqb6rA6sVrW6J/VMyjWHjlMUBX369Il/8MEHz44bN+6Ix+PB4MGDa44bNy66YcOG9i1btpgHDhyY5Ys8Ho8HmhsYdZYLZOZzAlj6/SHPe7kuAbPu6m4WLo+C//7txuwdivd4piK3ql/d+8bDzeQ+NyXAbNRVmqL4Sv061MdnP+6o+ueuk1MBPKN2HsZuFBE9GGqJbNCp0d2V9ve6NNxeF7b9u5YUoSxUOwuruNLOpmpmPPSrz4439uNbr1tTrly5Mkiv1yvjxo1LBwCNRoNZs2adrFWrVhONRiOEEEhMTLSMHz8+FQD27dtnbN26df2UlBTdww8/fObFF188CwAfffRR+MyZM6Pcbjc1b948d+7cucc1Gg1MJlOz0aNHn/ntt9+C33rrrVPLly8PWb16dagsy6JTp07Zn3zyyaniPh/uYsF8TghxzqjB5uUHLu9msWyvG20+syvh7zjw6T6T8tA97aVj8x/Bn+/dJw+5tSHMxso9cQARYc6zd5o0svQoEbVVOw9jN4KIqus0+g8NWqMxy5audpyAsvfEJmhkzT4evYJVNLt27TImJyfbCi4LDw9XoqOjXU899VRqr169Luzfv3/vqFGjLgDA4cOHDX/88cfBzZs373v77bdrOJ1O+ueffwxLly4N37Jly/79+/fvlSRJzJo1qwoA2O12qVGjRvadO3fub9q0qX3VqlVhhw4d2nPw4MG9r732WmpJsnILMisTFxz4ePY/ruT6VSho8u8u8VeKJCRZlkbe3hyf394YSTUj+MNZIaLCzPhsfE/jiLe+X0JE9YUQtuvvxZh/ISIKMhjmPnjzTdh+MkW8sngE+rQZJW5p2Jt4DOTr27D/h1yb0zpT7RyM+ZoQAkQkilh+1fbdunXLNBqNwmg0esLDw92nTp3S/Pjjj0G7d+82JScnNwAAh8MhVa1a1QMAsizj/vvvvwAA4eHhXr1erwwaNKjmHXfckVXSrhtcILOysnzLGemLDnNduPvmesrXDyXLHRrHQeKr43X165CIRb/vq7J6y7/vgMdGZgGIiAZHWiztXu9zl1av1WLFjp0Y+sXnYuPB1eL+WydIVUNi1I7ot+xOK/af3qoBsEztLIz5WuPGje3Lly8PK7gsIyNDSktL08myfFXhrNfrLy2TZRkej4eEENS/f//0Dz/88PSV2+t0OuViv2OtVovt27fvW7FiRfCiRYvCZs6cWXXjxo3FHkqVixVWJoQQOUT00yv3dxDz/9Nb7pRcE5LE458W1ydPdDfqNPIwIuqsdhbGSoKI6hi1mllfjXrQrNdqAQC9k5vg3FuvSc1qmPD60tH4adsCxat4VU7qn3YcWwetrF/HM2uyiqh37945DodDmjFjRhUg70a6MWPGxPbv3/98tWrV3Far9bp1affu3bO/++67sNOnT2sA4MyZM/LBgwev6qOZlZUlZWRkyAMHDsyaNWvWyX379pVoJB0ukFmZyba53p313Tar2jkCUXiwEfOe62UyG7SLiajijm/HKhQi0gXp9Stf6d3b2KJm3GXrdBoNvn1ktPTL42Owfu9SvLbkAeVU+hGVkvqv9ftW5dhdVh69glVIkiTh22+/Pfz111+H1axZs1Ht2rUb6fV6Zfr06ad79OiRc/DgQWNiYmLSp59+GlbUMVq0aOF48cUXT99666316tWrl9SlS5d6J0+e1F65XWZmpty9e/eEevXqJbVv377+1KlTS9Snn4S4qkWbMZ8gIsls0J5Z9959EU3jeYin0hjx1veOZX8e+Drb5hyidhbGrsei18+4Jb7uiB8eG2u61ox5iqJg5Lz5YuHmbdSx0d1Kz5bDJa1cuW/SBYCs3HRMXjjE7vG6qwgh7GrnYRXLjh07jiUnJ5+/+LMaw7z5ox07dkQkJyfXunI590FmZUYIoRh0mk8+/WHH0x8+1o2vfqUw/dHbDL9tP95Hq5FHuj3e/6mdh7GiEFHvqkFBIxY8MPKaxTGQ14r0xfBh9HjnTrjzw0/E1sO/iftvfZHqVGtYPmH91NYja4RG0q50e1xcHLMyF4jFbHniLhasTDnd3s/m/bxbcbm5v2FpBJn0+PG1ASa9Vp5BRC3UzsNYYYiopkmnnf/tIw+Zws3XnR/okuZxcTj1+hR5eJtGNOP7Z7D4z/e9TnflrQ3X7/ve6nDbZqudgzHGBTIrY0KIfzWytPe7jYfVjhKwGtSMwJxn7zCaDdofiChC7TyMFURE+iCDYeWkO+4wtqtTp8T7S5KEd/v3w+6Jz+P02U14aeF9Yt+pLWWQ1L+dOn8YF6xn3QDWqJ2FMcYFMisHF6yOD2Ys38o3692Ae9on4uFezUKCTLrviOiqmxEYUwMRUZBeP7djQkL8M11vu6EuewlRUTgydbL8bNdbaPbqyfji11e9NmeOr6L6vV93LrF7Fc97QgjP9bdmrFQURVF4OKkC8l8PpbB1XCCz8rB44/4Use/4+etvyYr0xgOddG0TazS2GLSf0vU6eTJWDvQazfPRYaF3LHpwpFGSfHM5ebFnT/w7dRLstoN4acF92HF0nU+O68+s9ixs/3cteRXPLLWzsApt97lz50K4SM6jKAqdO3cuBMDuwtbzKBasXBh1mpf7dUx8Zt5zvYxqZwlk2blONH/k89yT53JedLo976udh1VeRNQ33Gyat+PFCaaYsCJHZLohM9b8jme/WSnqVGuiDOn4tBxsCi+T86ht9T9fen/evugruyt3sNpZWMW1devWqhqNZjaARuAGUiCv5Xi3x+N5sEWLFmevXMkFMisXRFTFoJNPHZnzsKFGRJDacQLasbRMNH34c3tWrrO/EOJ7tfOwyoeI2pp1ul/XPv2UqXlc3PV3uAGZNhu6f/CRd8epVLnfzY+JNvW6VagvULxeD16Y19+W68y+WQixXe08jLE8/AmClQshRLpGkua+9/Vml9pZAl2taqH48bUBRrNB+xXPtMfKGxHFm3TaHxePeqDMi2MACDWZsPG5p+XZQwfimw0f4v0VTyoZOWfK/LzlZcex9VCEcoCLY8b8C7cgs3JDRLVMBu2+1EVjDcFmvdpxAt7vO47jzheX5uY63N2EEH+pnYdVfERU06zTbX63f7/w0e1vkcv7/DaXC3d99LGy9vC/Uq/WD4qOjfqQRIHdzjNt6WjrqfQjIwB8W9436MXFxf1msVjK/lNOgLBarSdOnDjRRe0czD9wgczKVYjZsPzFITfd+cyANoF9VfMTP27+F/e8/LXV5vR0EkJsVTsPq7iIKMas021+tc9dkeO6dC734rigVbt2Y/D/5ilBpiiMuO1FKSo0Vs04pXYkbTc+WvX8GafbHgPgVwAuAO8CWC2EKPTOel9KSko6vHfv3rplfZ5AkZSUdGTv3r3xaudg/oGLFFausm3Ol6ct2uDgiUN8o3urOvjyP70tJr3mNyJqpHYeVjERUTWzXrdh4h09I9QujgGgZ+NGOP/Wq1KbmkE0belD+GHrPMXrDbzR0X7cOi/X5XG+kt9y3BXAXACvA9hNRKOIiG9qZkwlXCCzciWE+MeriJ0L1+xVO0qF0efmepg9vmeQSa9ZS0T11c7DKhYiirDo9X893bVr1HO3d7uhsY59SaPRYOnoUbTmybH4e/+34tUlD4iT5w+pHavY0i6cwOHUXYoQyucAIIRwCSHmAWgG4FEAvQEcI6KXiShKzayMVUZcILNyl5XrnPTyvHVW7t7jO/d2TqIPxnYLMem164iottp5WMWQXxyvf7RTx+jJd/T0ywlqbo6vizNvTpV7NYrDu8vH4ZuNHytuj//fC/zTtgV2AeV9IYSt4HKRZ40QoheAjgCqAthPRJ+V9bdEp06dwl133YWEhATUrVsX48aNg8uV91pu374dq1aturTtSy+9hLfffrss4+D3339HSEgImjZtiqZNm2LKlCmFbnf06FG0adMGCQkJGDhw4KXMy5YtQ8OGDdG+fXukp6cDAI4cOYJBgwaVaW5WMXCBzNTwS3q2I+2HTf+qnaNCGdm9ifTm6M5hZoN2MxE1UTsPC2xEFGPR67eM6dih1ut97tL589BqkiRh9tD7aMvz47H/+G/i5UVDxZHUXWrHKlJm7nls+/cPeLzu/15rOyHEfiHEIwASABwF8DMRrSai23091p0QAn379kWfPn1w6NAhHDx4EFarFRMmTABwdYF8o7ze4nWza9++PbZv347t27dj0qRJhW7z3HPP4cknn8ShQ4cQFhaGzz77DADwzjvvYOPGjRg2bBgWLFgAAHjxxRfxyiuv+OZJsAqNC2RW7oQQItvmnPzSvHU8/bSPPdq7ufzpUz3CTXrNeiK6Ve08LDARUTOTTvfPxJ49ot/oe7dfF8cFNYmJwalpU+RRNzWlD1c9hwV/vON1uGzX37Gc/bZzqUsieZ4QIr042wshzgshpgKoBWAhgLeQ10/5ASIy+CTTb7/BYDBgxIgRAABZlvHee+/hf//7H7KzszFp0iQsXrwYTZs2xeLFiwEAe/fuRadOnVCnTh1Mnz790rHmz5+P1q1bo2nTpnjooYcuFcMWiwWTJk1CmzZtsGHDBjz//PNISkpCkyZN8PTTT5cqtxACv/32G/r16wcAGD58OL799lsAeR+cnE4nbDYbtFot/vzzT1SvXh0JCQmlfZlYJcIFMlPLkr3Hz9s37U9RO0eFc2/nJFr16gBLkFG3UiNLw9TOwwILEbXRAH9VMZsjHu3U0W/6HJfEm/f0xf7JE3Am/R+8tPA+7D25We1Il2Tmnsefe1d4nR77qyXdVwjhFEJ8ASAZwDgA9yCvn/ISIvqqmI//EdFVN1ru2bMHLVq0uGxZcHAw4uLicOzYMUyZMgUDBw7E9u3bMXDgQADA/v37sXr1amzatAkvv/wy3G439u3bh8WLF2P9+vXYvn07ZFnGl19+CQDIzc1Fo0aN8PfffyMpKQnffPMN9uzZg507d+LFF18s9Dlv2LABycnJ6NGjB/bs2XPV+vT0dISGhkKjyXurxsTE4PTp0wCAyZMn4/bbb8cvv/yCe++9F1OnTsXEiRNL+rKzSooLZKYKIYTb4fK89sqXf+WqnaUi6pgchw3ThxrDg4wzTXrtpAo19RgrM0R0p5Hot/dr1DBoHQ7ljhkfKe5ifhXub2pHRuDwK5PkF27vhM9+fkn87+cpXqsjS+1YWLnpfw4CPhFCnCjtMfL7Kf8ihOgJoDOAJQCWluBx1RByQggU9meiqOUAcMcdd0Cv1yMiIgJVq1bFmTNn8Ouvv2Lr1q1o1aoVmjZtil9//RX//pvXnU6WZdxzzz0A8opvg8GABx98EF9//TVMJtNVx2/evDmOHz+OHTt24LHHHkOfPn0KzXeli3m7du2KrVu3YuXKlfj222/Rs2dPHDhwAP369cOoUaNgs/nftwvMf3CBzFTjVcTsX7cd8247nKZ2lAqpYa1IbJ81whRXNfhZi1H7PyIKyNZAVvaIiHQkPREkSYs/j40zdQkKxtKYWPnAyZNi8Gf/8ypKmQ/JW2b+0+N2HJs6mTzOo3h54VBs+/ePQouq8pB24Tj+ObLG4/I4C7/brBSEEPuEEF+V4LFKFPICNGzYEFu2bLlsWXZ2Nk6ePIm6dQsfKlmv//8Jn2RZhsfjgRACw4cPv9Rv+MCBA3jppZcAAAaDAbKc13it0WiwadMm3HPPPfj222/RvXv3q44fHBwMi8UCAOjZsyfcbjfOnz9/2TYRERHIzMyEx5M3zN+pU6dQo0aNy7ax2WyYM2cOxowZg//85z/43//+hxYtWlxq2WasMFwgM9UIIaxOt/f5R/77Uy6PaFE2akQEYfOHw83N46sNCDLpVhORRe1MzL8QkcksSUuiddqpy2rWMjUx5g29a5AkLI2OkX/bs5eeWLI0MJuR81ULCcGOic/L7/fvjQV/vC0+WvW8Nyu3WN1/fWrZXzNzFaFMFUJklPvJr+PWW2+FzWbD3LlzAeTdRDd+/Hjcf//9MJlMCAoKQk5OTrGOs3TpUpw9exYAkJGRgePHj1+1ndVqRVZWFnr27In3338f27dvv2qbtLS0Sx9mNm3aBEVRUKVKlcu2ISJ07twZS5cuBQDMmTMHd91112XbvPnmmxg3bhy0Wi3sdjuICJIkcQsyuyYukJmqFEV8uvf4+XMrNgTO+KWBJsikxy9vDjL1vbneTRajdg+PcMEuIqI6JpJ2dDCbey6rWcsco9Ndtj5co8GCGtHSnL/+kqb9uDpwm5HzjW7fHqnTplCYLpOmLB6ODft/KKwxtUwcSduNI2m7HF7Fc82RK9RCRPjmm2+wZMkSJCQkoF69ejAYDHjttdcAAJ07d8bevXsvu0mvMElJSZg6dSq6deuGJk2aoGvXrkhNTb1qu5ycHNx5551o0qQJOnbsiPfee++qbZYuXYpGjRohOTkZjz/+OBYtWnSp+0TPnj2RkpJ3D8sbb7yBd999F/Hx8UhPT8cDDzxw6RgpKSnYsmXLpaJ5/PjxaNu2LebMmYPBgweX/gVjFR5PNc1UR0TdalSxfH103iNmnVb1SboqtLk/7xZjpq+2O93epzxe5ZNyqw6Y3yGi7gair56KjDQNCQ2Tr9VNfbfdjhFpqfhg0EAx4qZ2FaI/++ItW/Hg/EWiakgtMazLf6SI4Opldi4hBKYte8h6Ov3fsUIoc8rsRCXEU01fjqeaZgVxCzJTnRDiJ6vdtfWD5VsD+mvcQDCsayPa8uH9ptrVQt4JMum+JaJgtTOx8kVEkkGSJgdL0rJPY2KD7gsLv2ZxDACNjEa8HRGJsQsX0fe7/Hd84ZIY2LIFzr35KtWLILy25AH8tmOpoihl8ydo57H1OJ+dchYQ88vkBIwxn+MCmfmFbJvrkZfm/ulKz7arHaXCS4yrgh0fjzQP6JjYzWzQ7ieiFtffi1UERBRjlqR1dXW6Z1bUrm1qUcjIAUXpGBSE/4RXwYBPP8NfR46UYcryY9Dp8MNjY6QVj4zCbzu/xBvLHlZSLxzz6Tm8ihdL18/IdbrtY4UQ3AjAWIDgApn5BSHEXggsmPjFWqfaWSoDo16L2U/1NHw2vmc1s0H7p16reYKHgqvYJKJBBqJ9I8LCWy6qWctcVVPymaPvCQ3FA8HB6D59BvamXN2vNFDd3jAJ596cKt1SJ5ze+noMVm2Zo3i9Hp8ce8P+VcLuyt0D4EefHJAxVi64DzLzG0QUadRrjv7z0QhzYlyV6+/AfOJIygX0nrQ09+S5nK05NtcwIcTVt5yzgEVEkWZJ+ixYkrr8Nzra3MhgvOFjvpyWKn71eOifCf9BbHi4D1L6j43//oveM2crIBONuG0ixUXWK/WxnG47Xpw/yG53WTsIIbZcf4/yFRcX95vFYolTO4e/sFqtJ06cONFF7RzMP3CBzPyKTis/27FJ3KSf3xhkVjtLZeJye/HG4o2e1xdtcHk8yoturzKdvw4OfETUz0A0e0BoqPGJiEidQfLdl4ZjU04rR7Ra2vrC81TFUrFGD1QUBWMWLhafb9hENze409ur9QOyTqO//o5XWLVlrue3nUtW2V25d11/a8aYP+ECmfkVItJbDNpj37x8T7XbmtdSO06ls/9EOoa+sTJ397Hz5x0uzwAhxCa1M7GSI6J4iyTNCpKktu/UiDY3Nd54q3Fh7j15wquEhdKGZ5+RzPqSF5D+bk9KKnrMmOXNdijS/bdOoIQaycXeN8d+AZMXDHG4PM5GQoiK0WmbsUqE+yAzvyKEcFod7kcf/u+PuV5vwA+7GnAS46rg6f5tzC7y1pQM0h+yQZ5FRCFq52LFQ0QWoyS9bSTaOSq8Sqcfatcps+IYAL6MjpFzMi6IQJ6S+loa1qiOE6+9LD/aoSXN/OEFzP/9La/dlVusfb/eMMsO0OdcHDMWmLgFmfkdIqJgk27zm6M6N3/ozmZ841g58ngUBPd/Twm9J4JCWoZQ2uI0R9amLIdwiychMI+7XfgnyrvDcrCB6INOFovhuapVjaW5Ca80bIqCO0+eUNonJYlFD46UJR924/Anx9PT0W36R97TWbnysM7Po2FcmyK3PXh6G2b9+GK6y+OoLYS4/vRzjDG/wwUy80tE1CLErP/zxJdjjMHmivfVrb8a9Oq3YvmRY6Luy3UlkvI+m9iO2JAyN8XqOuPKUBzKUwC+5glG/AcRtbRI0uwojSb+lWrVy7TFuCgZHg96nTqpDL2pnfjvwAEVeraft3/6GRNX/igSY1oqg9o/KVuMl3/B4va48PKiobmZuecHCyFWqBSTMXaDKuZHfRbwhBBbFUV8/eSsXx1qZ6ksdv57Bkv+OkAxo2MuFccAYKprQt2X6lpix8TG6arpvpAM0n4i6sHDwqmLiJoEyfLqYEn645nIqk2W16qtSnEM5E1JvbBGtPTF+r+kNyrAlNTX8nS3rjjx2ktEnhN4edFQbDn822WfF3/4Z57b4bKt5eKYscDGLcjMbxFRiNmgPbxs0t0Rt7eqo3acCi9m6IdeRxMdqg+uXmQLoFAEsrdmI21RWq7X5j2k2JVxQoi15ZmzsiOiZIskfUpAo0eqROgHhYZKvhyd4kbsstsxMi0VM+4dJO5v17bCf4D6bP16PL74GxEbmagM7fysbHfl4s1lY3LdXmc9IUSK2vkYY6XHBTLza0R0W5Vg4/JDXzxkCgsyqB2nwpq2aAMmfrUO9d6uD0l//WJLKAKZGzJx5qszuYpL2aXYlVcA/CiEqNCth2oioluMQYapIGqtsbsNE6pEUu8Q/7t/8vecHDx97iyWjB6Fno0bqR2nzOXY7bjjo4+Vv4+ekIKMoe4L1nNPeBXPR2rnYozdGC6Qmd8LMuk+7dm67pDFL/ZR5/vjCi7T6kDkvdNFjYdjKLhpcIn2VTwKsjZm4dx353I8mR6r4lLehILPhRBZZRS3UiEiCUB3Y5BhqlavqXfnE91M7fq1oJ8++QP/fLBGrK5dhyQ/7OmyNDMTb1zIwC9PjEPbOrXVjlMuRs2br3y5actxu9sVzx8UGQt8XCAzv0dEZrNBe3Dus3fW6Nu+vtpxKpybnpyn7KIsUWt8rVLfXCWEgP2wHed+OGez7rISSTRfcSrvCCEO+DJrZUFE9TR6zQOSLD0QFhWsu2Nc16AWdzSBrMn7X6QoCp5Pmqi8VCVS6h5Usg815eWj8+cwNzcXG597Bg2qV1c7Tpk6cu4cmrzyqt3mcjXj9zxjFQMXyCwgENFNIWb9Lwc/H22sGsaT7PnKqr+PoNeUpUiYVg/acN8MC+a+4EbGrxnu9F/SPZDwj2JTZgBYKYQo3gCylRQRhQIYaAw2joUQddvd00K+aUBrXUyDwovLle//hB0f/SF+9NNWZACYnJYq1ni99M8Lz1e4KakvUhQF7d58O3fHqVNTHG73m2rnYYz5BhfILGCYDbr3OiXHjf5uaj8TD6Bw4xRFQeiA/yrmnmEUcXuEz19QxaUge0s2Mv7IyLEfsetIR78oNuUzAD8IIXh0EgBEpAHQ1RhkGON2ero2uCXB02FIW3NSh3qXWouLcrEV+ZUqVaWuQUHlE7gUxqScVo5ptbR1wn8o3FzxPtzOWPO78sLyFXtyHI5mPE44YxUHF8gsYBCR3mLU7f/o8W41h97WiCvkGzTq3VVi7rb9SHg1gUgu25fTY/Uge0s2Lqy9kO044dCSllYpduVzAD8LIVxlenI/Q0QygJZag/ZeIgyvEhMudxzaztKqV1Myh5pKdKzlb/+IPZ/8KVb5cSsyAAw6ecJLYWH013PPSCadTu04PlOga0VzIcR+tfMwxnyHC2QWUIioucWoW7fvsweNMZH+2fcyEBw6nYHE0Z+i1vO1YapTsqLsRrkz3cjenC0u/Hkhx5ni1El6ab0317sMwGohxL/lGqacEFEMgG7GYGNft9PdKSQiSGlxRxNDu/4ttdXjo0p9XEVR8FyDF8WrEVF0mx+3InsUBb1PnfTWjo2l1eMek7Ry4M8l4nS70fy113MPnz030enxvKd2HsaYb3GBzAKOUa+d0iax+vg1bw/mrhalVHfkLO+FOoToEdGqViqeLA+se6zI3p5ts+6yEgRyQFij2JUfAfwB4FggztpHRHEA2uvN+m5E6KIoIjLxpnhPcteG5qT2CQirHuqzc33z5irs/+wvsapWbb+eu8WmKLjj5AmlU6OGYuEDI2V/zvp/7d15eBPl9gfw7zuTdE1LkZ2WXRHKViggCEV2FBEBvezLVdQfXFwuIm7siwvIoiCroHCRRWQTuQqoFYVrEVC2WqCUNpRSoC10SybJJJnz+6NQQYq0JW2S9nyeZx7ayZuZkyHpnLxz5n0LY9zGTbZ1vx76KddqfdQb36OMsb/HCTLzOkIIfVCAz/G5z3d5cEyflp4xQ4IXWbrzN7y8OhoN5zWEHOA5PXlEBFuqDeYzZphOmEzmM2aZnKRKeumUpmoxZKffARwHcMZTyjKEEL4A7gfQEMCD/sH+D2kOZ0chhKFBm7r28KiGQfe3qYda4TUhySXzVtU0DW80mkzvV60muhg8txcZAK46HOibckEb1eFhWjjwH57z5iuiHceOYfina9LNqvogEWW6Ox7GmOtxgsy8khAiPMBXf+TkJ6P967uwN66sMykq7hvyEVX/Z01Roa3nTTJxMyKC/Zod1mQrrBesZEm0mCznLXBkO/wlPykFwO+aoh0DcBFA6vV/LwLIdGWP3vWxiENxPQn28dc31fvpIxyq83671X5fcGWDUq1+FQprXMO/ZsPq+gat66Ja/SoozR7Sre/+FwlrY7RddetJnt4ze15VMfBiCk15vDdN7NXT677gGjOuotms2RaTzdaViA66Ox7GWMngBJl5LT8f3cRGtStN+3XRyEBfH527w/EKPd7cpMWY0lD3Tc9PpO5Es2mwpdpgSbZATVOd9gy7Vc1QHY5Mh+TIdfiSk4TkI10TOnEZQCYACwhWIlLghEIaKeQgMwgWAAISAoVOGECo5efvZ5Bk4QNCiKZpFZwOzeCwOYJ8A3zsVepUUkMfrO5T88Hq/tXqVUG1+lVQudZ9kPXu7wh1OBx4M3wqfVC1unjEYHB3OHd13GLBc5cvYenQITSy3UNe80a0O51o/e775jNXrsyy2u1z3B0PY6zkcILMvJYQQgoK8Nk96JHGnT559TFfd8fj6X4+kYwub23E/bMfgE/VsjOSwF85rU44shywZ9qhKRo0uwZyEMhOIAfl/W4naHZNE0IIoRdC8pFwNfoqwiMaosM/2sA/2B8BwX4IqOCPgAoB8A3w/OP15eyvYVz3q7bTC3qRASA6Nxevp6dhy/+9gMeaNnF3OIUy/sst6ur//fK/XKu1O8+Wx1jZxgky82pCiAqBfvrYxS/2CH2mV3PPzwrcRNM0VB6y2KnvHCSq9q3qdZe1S4NxgRFtIyO0gVP7euXxyetFnkLzqtYQnbygFxkANmdl4YPMa/hh/Ct4qJ5nT0n935MnMfCT1RmKqjYioqvujocxVrK88kTA2A1ElG222nu9uHiv5fezl90djseauPJHmCSHVLl3Zf7M34HkL0HJsXhtj4FOp0ObYe3E/PQ0zVs6PgaGhGBUUBB6frQYpy5dcnc4d5SSmYmhqz+zKKran5NjxsoHPlkyr0dEcVbVMeqxtzcrGdmKu8PxOCnpOfhw128Iez5MSDr+yN+J7C/DarJ6R2Z5B0+99ThSNaf4RfGez8GLlaugp68vPTJ/IVIyPW9ACLvTiX7LVphVp/M9Ijrg7ngYY6WDz5asTHBqtMVsta/sP32b4nRyaeDNHp282RnUMsgZ+GDZm+bXlSR/CZZcm7vDuCc6Hx1aD2kr5nlRLzIAzKxeQ4RDaFHz5tM1s9nd4dziXxs32eLT0g5b7fZ33B0LY6z0cILMygyz1T7xeGLa8VdXRHvEGLme4PPvY3Hq0jW5xvAa7h9qwcPJgTKsZpvX17H/Y/ITSHE6RIwX9SIDwLLQUCnIYtW6LfxIU1TP+Ah/vO8n56bDRy7lWq1P8k15jJUvnCAzryfyvAng7VxF7bP62+MZq745Xu5PZqrqwPMf79aqD61OOgMPg3c3ukAdbGUgQdb56BA5qI1X1SLfsD40TM66epWeWLLM6XA63RrL96dO441t28wmm607EeW4NRjGWKnjBJl5NSFEIICNAAYA+JSIrpmt9q6vLP1O+flEspujc69B735FoqoeFTtW9PqkrzTIBhmqRS0Tx+rpyX2Q7HSIQxbv6kXWSRK2hNWS/zh/Xoxas9bprgQ//soVDFixwqKo9ieJ6JxbgmCMuRUnyMxrCSHqADgAQAXQiYhSAICIzig2x4AnpmyxJF7KcmeIbnMk/hK+OpQgwp4P84oxcT2BbJChWu3uDsMlfPx80PLpSDEvPd3rrqQESBK2hIZJ3544KV7bsrXUu5GvmkzotvAjxaLa/01E+0p7/4wxz8DXXe+idu3a0QaDoba743A1k8mUnJyc3NXdcRSXEOIRAJsAzAXw4V+nFiai73z1uje6v77x/WPLnw0IDixf84j0mb7FWalnJeFb05e/BBeSzqCD3WovM98mBk7ri7e+PCIOKwraBAS4O5wiqaTTYUPNUGng/gNUIyREe61H91J5H1vtdvRa9LH5mqJ84tC0HUKI9wDMICJraeyfMeY5OEG+C4PBUDsuLq6Bu+NwtfDwcHeHUCwirzt0LIBpAIYT0Xd3amuzOxYH+fu06Dd965C97w8O0MnlI1ec8Z8DuGq3SQ2frFNmkr3SoKugg8PmcHcYLuPj54MWA1ph3tex2hd16nrdm7+Ojw+WV68uXvh6l6gWHEQjHirZKak1TcPQ1Z9a4tOuRCuqOgF558cGAH4QQvQjovSS3D9jzLN43R9NVn4JIXwBrATwLwAP/11yfIPJah97JP7y4SHvfGUpD8O/ZWQpmLX5Fwp9LlRIPvzxLgopQILToUErQ++TwdP7iXN2VRzxshEtbmjpH4D3K1fBmM83iD1/xJXovt7YvkP9/tTpM7lW20Ai0ohIBTAYwI8AfhVCeGevAmOsWLgHuQg6d+6Mt956C7169cpf9+GHHyI+Ph5NmjTBJ598kr/e4XDgjz/+QFxcHBo3bnzLdh599FEcPHgQHTt2xK5du/LXJyUlYfDgwbh27RpatWqFdevWwcfH57Y4kpOT8dxzz+HChQsQQuCbb75B3bp1MWzYMJw8eRJ9+vTBu+++CwCYNWsWmjdvjieffNLVh6NUCSGqA9gKIA1AeyLKLczziMguhHhsz5GkH0fP/6bFZxMf9yvLNbmPTdnsDAgPRFDTIB7WrYgknQRZL0O1qPAz+Lk7HJfwCfBB8ycjsODbU9qG2nW88htTt6AgTHQ68NSKlYge/2+0rVfX5ftYuu8nbdnP+9PNNluPm8sprg/tNlkIcRbAPiHEUCL63uUBuIm3lxB6e6kg82ycIBfBkCFDsGnTplsS5E2bNuGDDz5AVFQUxo0bl7/+7bffRkRExG3JMQBMnDgRiqJgxYoVt6x/4403MH78eAwePBhjxozB6tWrMXbs2NueP3LkSEyaNAk9evSAyWSCJEk4ceIEAODEiROIiopCdnY2FEXBoUOHMGXKFFcdArcQQrQGsA3AagCzijoeKRFZhBDdtx2IPxDop2/08Us9fctikrz9wBn8npQmPzDnAXeH4rUkWYJNKTsJMgAMnTVAvPnVVBy1KGjp7121yDcMCqmINIcDPT9ahINvvo5G1au7bNtrYmK0idu2Zyqq2omIMgpqQ0RrhRBJADYLIaYS0UqXBeBG3l5C6K2lgsw7eGWPgrs8/fTT2LVrF2y2vNm2jEYjUlNT0bFjx1va/fzzz9i8eTOWLl1a4Ha6deuGoKCgW9YREaKjo/H0008DAEaNGoUdO3bc9ty4uDg4HA706NEDAGAwGBAQEAC9Xg+LxQJN06CqKmRZxtSpUzFz5sx7fdluJYQYDuBbAC8T0YziDtZPRKZci/rIuu//SJz4yY+qt40PezcOh4bhC/6rVftHNdKH6N0djteSdBJZzd49m95f+QT4oGmf5piXnu7egYXv0UuVq6C7ry91mrcAFzOzXLLNDYcO07iNX2QrqtqBiBL/ri0R/QwgCsAEIcQ8IQRfpWGsDOMEuQgqVaqEtm3bYvfu3QDyeo8HDRqEm3sjs7Ky8Mwzz2Dt2rUIDg4u9LavXr2KkJAQ6HR5nfphYWG4ePHibe3i4+MREhKCAQMGoGXLlpg4cSKcTicaN26M2rVro1WrVhg4cCASEhJARGjZsuU9vmr3EELohBDzAEwH0IWIdtzrNokoO9eiRq3YdTRlxrr/lY3xvK57Zv5/yRkscF/X+8pe13gpkmRBNsUzZnFzpaGzB4jTNpt83GJxdyj3ZFb1GqIRoHWaP58yzfdWV7316FF6/vP1Odd7js8U5jlEdBZAewCRALZeH4edMVYGcYJcRDfKLIC8BHnIkCG3PD527FgMHz4cHTp0KNJ2C+rRLKgMwOFwYP/+/Zg3bx4OHz6MxMRErFmzBkBePfSxY8cwYcIETJkyBTNnzsQ777yDgQMH3lIf/Zd9/CyEyPa0BUA2gOYA2hJRbJEO5t8f56smi73DvC9/vTL3i4NlYsiCuPMZ2PBznAh7IUwSEufH90LIAmoZTJD9DH5o0rsZzUtP8+peZABYVqOmFKBYtW4ffqhZijkl9X9PnsTIz9aaFFXtUtS/L0R0DUAvANcA7BdChBYrCA8kyzIiIiLQpEkTtGjRAgsWLICm5V2027dvHypUqICIiIj85fvv71yO3bdvXzRt2rTAx65evYouXbrAYDDgxRdfzF9vs9nw6KOPomnTprdcgX3hhRdw9OhRF71KxgqHa5CLqF+/fnj11Vfx+++/w2KxoFWrVvmPrV27FkajEevWrSvyditXroysrCw4HA7odDqkpKSgZs2at7ULCwtDy5YtUb9+/fx4Dh48iNGjR+e3+eqrr9C6dWuYzWbExsZi8+bN6NSpE4YNG4aA28dD7QnAUwcJzvnr+MauQESXhRDtZ37+vyMBfvrKLz4Z6dWXSh+butkZ0qki/Ov4e/Xr8ASijPYgA8DQd58SkyKmyycsFjT393d3OMUmSRI2hIbKT6RccPZdutz57UvjZJ1c+Lf+d3GnMPCT1WZFVXsQUbGyLiJShRCjAbwO4KAQ4kki+r042/Ik/v7+OHbsGAAgLS0NQ4cORXZ2NmbMmAEAiIqKuuXG8jvZtm0bDAbDHR/38/PDrFmzEBsbi9jYP7+f7NmzB5GRkfjmm2/QqlUr/Otf/8Lx48ehaZrXXg1l3ot7kIvIYDCgc+fOePbZZ2/pPU5MTMSkSZOwfv36/DKJohBCoEuXLtiyZQuAvGS7oJEn2rRpg8zMTKSn5w3JGR0dfcuNCna7HR999FH+jYA3eqFv1Cb/FRFZiSjbQ5cSKxQmohSz1d7+jU/2ZX6254TXjuu1YMshXMwxy9WersbJsSvIKHM1yDcEBPkh/LGmtCDDu2uRgbwpqbeG1ZJPGI1i1Jr/FHpK6p/i49F/+QqzoqqPEtGv9xID5ZkD4N8A9gghvHuooL+oWrUqVq5ciY8//rjAK5x3YjKZsGDBAkyePPmObQIDA9GxY0f4+d16M+yNe2kcjj8v7t24GspYaeMe5GIYMmQIBgwYkF9qAQBz5syB2WzGgAEDbmm7ePFiREVF3bIuKioKp0+fhslkQlhYGFavXo1evXphzpw5GDx4MCZPnoyWLVvm9wofOXIEy5cvx6pVqyDLMubNm4du3bqBiBAZGYnnn38+f9tLlizBqFGjEBAQgObNm4OI0KxZM/Tu3RshISEld1C8EBElCSE6vrh470E/H12FIV3Cvao+IcdsxRtrf6LQ50OF7Mf5sUvIQFntQQaAYe89JSZFzJBjrRY09fPeXmTgzymp+544Qa9v2+784KkBf/shiElMxOMfL1XMqtqXiA64Kg4i2iqESAawQwjRGUCqq7ZdQkwAVhTmhuf69etD0zSkpaUBAPbv34+IiIj8x7du3YoGDW4dBGPKlCmYMGFCQVcr76pHjx5Yt24dHnroIbz++uvYuXMnIiMjC7yaylhJ4wS5GPr373/bN+oVK1bcNmzbnezfv7/A9fXr18ehQ4duW9+6dWusWrUq//cePXrkD+v2V//+97/zfxZCYOPGjYWKqbwiojNCiE7Pzf/mgL+PLrhfh4buDqnQnpi+VfNr4E/BkcGcHbuKToiyWIN8Q0CQPxr3CKcF+xO1T2vV9vr3TWWdDutr1BSDf/pZqlGhgvZq924FXhU9bDSi10eLFbOqPkVE0a6Og4gOCyHaI2+Wz6qu3r6LFSlzvflcd7cSi2PHjiEhIQELFy6E0WgscmA6nQ4bNmwAkHc1tFevXti5cydeffVVJCcnY+TIkejbt2+Rt8tYcXCCzMo9IjophOg69L2d+za+3dfw5MOenyR/dyQJB+JSpPvf4zGPXUoHYVPKZonFDcPnPi3ebjlTjrNaEe7n/eM91/P1zZuSeufXolpwEA1r2/aWK0H7zsTjiaXLFJPNNpiIdpdUHESUDOCtktq+OyQmJkKWZVStWhWnTp26a/uYmBj89ttvqFu3LhwOB9LS0tC5c2fs27evyPteunQpRo0ahZiYGPj4+OCLL75A+/btOUFmpYZrkBkDQES/WWyOLkPf3Zn18Ve/eXSNpqZpeHrODq1Kv6qaT6XbZ1pk98AHktVUthPkgOAANOrWiBaWgVrkG25MSf3CuvVib9yfU1LvPH4Cjy9ZajbZbH2I6Gs3huh10tPTMWbMGLz44osFjqhUkLFjxyI1NRVGoxEHDhxAw4YNi5UcZ2ZmYteuXRg5ciQURYEkSRBCwGq13v3JjLkIJ8iMXUdERxSbI/LNVfsuvbYyWtU0z5xM5KUl38HqQ6Jyz8r8+XUxyVeCJcfitTdtFtbwuQPFbxZFPl2GEo5uQUF4reJ9GLB8JQ4bjVgbc5AGr1qdo6hqZyL60d3xeQOLxZI/zFv37t3Rs2dPTJs2Lf/xGzXIN5YbN5UXxs6dOzF16tT83+vWrYtXX30Va9asQVhYGOJu+mIzc+ZMTJ48GUII9OrVC0eOHEGzZs1uud+GsZImytqMYq4WHh6e4M1Tcd5JeHj4ubi4uPvdHYcnEkJUCvL3+b5X63oPfv7mE/6+Pp5TiZR0KQv3P7cSdV+vi4D7vXPaYE92YcUFPFitvvPZhUO8vj73bpaN/oyCfk3WVobVKlOvdVF6GtaZTCDgmqKqHYno7rUB5ZS3n9/4PMZKkuec+T2UyWRKLovzvZtMpmR3x+CpiOiqEOLhPUeStnZ+bcMj3747MCDE4Bm1mr2mbHYGPxSMgPsDylRS4ylkfxmWXGu56DUY8cE/xOQ2s+QzViseLAO1yACgEcFCpDpU9ZoN6MbJMWOsuDhBvovk5OSu7o6BlT4isgghnjiZlP5x5L/WjNg3b2hgraqFnzq8JKz65hgS07Pkhq8/6NY4yjIpQII13eZVw/0Vl+E+Ax7o1FBbeDiFlpeBXmRV0zDx0iXLL4r5zPXk+Jq7Y2KMeS+uYWTsDojIabKoYy9m5E5vOfYz5fi5K26LRbGqGLfie6o+vAbJgV6fy3gsOVCGrYxOFFKQER8Mkg5ZFPmszbtfc67TiVEXks2/KOafzJr2MCfHjLF7xT3IjN2FVXXMkySR3PHfn3+2fcZTAd1b1S31GJ6evUPT1dQjpH0If6ktQXKgDJuiloseZAAIrmzA/R0f0D78PZWWhIZ55TevS3Y7nrmQbM5wODYoRGOJqMyMzlHSvL2EkEsFWUnim/QYKyQhRFSAr27X0pd7BY3q2azUkqiYuBR0mLge98+6H77VfEtrt+VS9u/ZMH2ZQ+/9MqncJMnZ6TmY9tA72FynLu739a7316+KGS9fvGixEU1XiT4oyenpGWPlC/dGMVZIRLRfsTnajVu8N33GugP20joXPzFzm7PyY5U1To5Lns6gg2q1l5vkGAAqVAlG/YcbaB950bjIRIRPr111jk1Jyc7VtD42TZvLyTFjzJU4QWasCIjolNlqj5j/5aHEwe98ZTFZSnZa4rdW7UMO2aUqfarwZ7UUyEEy7Fa7u8ModSPnD5IOmM1yohfUIps1DS+nXrQsu3o13krUvCSmjmaMMT7pMlZERHQp16JGfnMocUeT51YpJxLTSmQ/l67m4oMdhxD6fKiQ9PxRLQ26YB0cqtd0pLpMSLUKqN+uvrYoI8OjX7xRVdHfmKQcVJTtZk1rdX16Z8YYczk+6zJWDERkzlVsQy+k54xp//J/lOW7jmquvsL76JQvnYEtDE5DY4NLt8vuTPKXQBrBaffoPLFEjJw/SPrJbJKNasleFSmuH025eNqYZLlst08wa9pwIio70wAyxjwOJ8iM3QNNo3WKzdFq4sofk/pP32bJNrvmnP3Fj3GIvZAh1xxe0ytHFvBWkiRB1kuwlXDpjCeqWCME9drW0xZ5WC2yRoSP0tPtr6WmXlWIutqJlnO9MWOspHGCzNg9IqIzJova7Iejxo2Nn/1EOXLm0j1tT1UdeGbRt1r1wdVJF8wjMZY2SZbK1VjINxsxb6D0o8kkn/eQXuRspxPPpVxQPs/KPGEhakJEB90dE2OsfOAEmTEXICJLrqKOvpxp/ucjE9abPtx22FncTq7hc78muk9GxU4Vy9VoCp5C0klkUzwjQSxtlcLuQ93IOtpiD+hFPm6xoG9SonLCYllr1rT2ROS+mXoYY+UOJ8iMuZCm0ZeKzdFi6pr98b0nfalcy7EU6fknEq9ga0y8CPu/MElInB+7g5DLb4IMAMMXDJJ+MJnkC27qRbYTYVFGuv2ZC8m56U7nSLOm/YuIyt/QIowxt+IEmTEXI6LEXIsaceDkhTWNR3+ixMRdLPRzH532pfO+rvdpfqF+JRgh+ztCBpXXEgsAqFKrEuq0rK0tvlr6I1oYVRX/MBrNn2dmHrQSNSKiraUdA2OMAZwgM1YiiEjNtajj0rKUId1f35j73sYYh6b9fcnFextikK5Ypar9q/Hn0o2ELFCee5ABYMSCQdJ3ubnyRXvpHAciwhdZmdpTxiQlUbW9bdK0R4gotVR2zhhjBeATMWMliIh2KjZH0/c3xcQ+/Mo689mUawW2u5ajYOrG/RQ6OlRIvvyxdCtJkE0pvz3IAFClTmXUah6mLS6FcZFT7XaMupBsnpeeHm8ham0nWsSjVDDG3I3PxIyVMCJKzlHU1kcTLk+NGPOp8u6GXxx2x615x+NTt2r+DwZoQS2C3BQly6cDbAqXvI5YOFjak5srX7SXzLEgInyZlUVPJCVaTlitH5g1rTmATkII/xLZIWOMFQEnyIyVAiJy2uzOBYrN0WTOFwdjmjy3ynxjOLhdMWdx6GyqVPOfoTzmsSfQkSjPNcg3VKtXBWHNQrWlJdCLfKPXeG562hkLUVubps24fiPeIwDecfX+GGOsqDhBZqwUEZExR1EfSUjNHNNpwvrcl5fstQ2Z97VW9alqpK+od3d4DADpIJX3GuQbRi4YIn2TmyNfclEvsp0Ia69d055ISrSctFrnmDWtGRHF3tTkJQCDhRCPuGSHjDFWTJwgM1bKiIg0jT632BwNVu8+EW3KUSX9fXrBZZeeQfgIYTVZ+T8DQLUGVRAaXtO51AUjWvxiNuOxxHPmJVczYixEkVZNm0VEjpvbENFVAC8AWCOE4HojxpjbcILMmJsQUbrZYu8NoPvF1RcvGOcazbY0vrTvbpKfBCXXqrk7Dk8xfMFgeVdOjny5mL3IF1QV/5dyQXn5YsrlVIdjmEnToojo1J3aE9EuANEA5hczZMYYu2ecIDPmZkT0g2bV7lfOKu8mTE5Qrmy74tBUzs/cRfKXYCniBC9lWc2G1VCzUXXnsiL2Iiuahg/T0+1PGpOUQ4oyRyGqR0RfFXKEivEAegohehcvasYYuzd8WZcxDyKEqCX5S8slH6lzjRE1AoIjgyEEz6hXmi6tv4Qa9sqOFz8brXN3LJ7i4ulLmNv7Q+ypXx9VdX9fK09E2J2bi9lpVxQ70R6Tpr1MRClF3acQoguAdQCaE1HB4yOye1K7du1og8FQ291xuJrJZEpOTk7u6u44mHfjEwBjHoSILgB4XAjR8+Kqix+nbUurUX1IdYOhqYET5VIiBUiwpNr4YN8ktFENVG9Yzbn88lVMrV79jqOtnLZaMfXKZVOSql4ya9qzRHSguPskoh+FEFsBfAxgaHG3w+7MYDDUjouLa+DuOFwtPDzc3SGwMoBLLBjzQES0V7NqjWyptmcvLLlw4dyMcybzGbO7wyoXpAAJNrPKCfJfDF8wSN6eky2nOxy3PZbmsGPK5UvWocnnc+Os1olmTWt8L8nxTd4CECmE+IcLtsUYY4XGCTJjHoqINCL6UrNq9a3nrS+dX3A+LfG9RLPFyPWxJUln0JX7qaYLUis8FNXur+pccVMtcrrDgXeuXLH1Sky0fJOT84mVqI6TaDkRuWTsZCJSAIwC8LEQorortskYY4XBCTJjHo6IHKTRGs2m1VLilTcS303MNC40mq0Xre4OrUzSBeqgWrgHuSDDFwySt2Zny2dtNsxJu6L2TDxn3Zad9ZmNqIGSV2uc6ep9EtFBAKsArBRcZ1Rirl69ioiICERERKB69eoIDQ3N//3KlSsYOnQo6tevj8jISLRv3x7bt2/HuHHjEBERgfDwcPj7++e337JlS4H72LJlC4QQOHLkyN/G0rdvXzRt2jT/98WLF6Np06bo3bs3VDXvy+uBAwfw6quvuu4AMPYXXIPMmJcgIhXAEiHEZ+ZY84vn4s5NDmoZpKv2dDV/36q+7g6vzJCDZditdk7EChBSPQT+Ffy1AcYk4SvE5zaiacW5Aa8YZgA4DOCfAD4rhf2VO5UqVcKxY8cAANOnT4fBYMBrr70GIsLDDz+MUaNGYcOGDQCA8+fPY+fOnViyZAkAwGg0ok+fPvnPL0hubi4WLVqEhx566G/j2LZtGwwGwy3rVq1ahRMnTmDKlCnYs2cP+vTpg1mzZmHTpk3Ff8GM3QX3IDPmZYhI0RzaXLJTWO7vufMTJiUoKatTrGo6lwW4gi5IB7vt9jrb8izzcjY2TdthmxT1nlVR7WudQFOzpo0upeT4xpfDEQDmCiHqlMY+WZ7o6Gj4+PhgzJgx+evq1KmDl156qUjbmTJlCl5//XX4+fndsY3JZMKCBQswefLk2x6z2+1QFAV6vR7r1q1D7969UbFixSLFwFhRcA8yY16KiHIATBFCfJj9a/bb2Qez/y/wwUCq3LuyIbBxII96UUxykAyH6gARlftjePH0JXy36mfLb7uOQ8jSp3ar/V0iSnVHLER0QgixAMAWIcR37oihjDABeJ+ICjXY+h9//IFWrVrd0w6PHj2KCxcuoE+fPpg3b94d202ZMgUTJkxAQEDALetfe+01tGvXDk2aNEGHDh3Qr18/7N69+55iYuxuOEFmzMtdn553ghBiqinWNFxJUN6SDXKlKo9XCQx5OERIvnyhqChkv7xRzByqE3rf8vcnUnNqOPHDKexZ/mPuxVOXnJqmfeRQnUuJKM3dsQH4AEAugGB3B+LFTACKPQHCuHHjcODAAfj4+ODw4cN3ba9pGsaPH481a9b8bbtjx44hISEBCxcuhNFovOWxESNGYMSIEQCAGTNm4OWXX8a3336L//znP6hVqxbmz58PSeK/c8y1yt9ff8bKKCIyA1ghhFipWbUul7+8/NaljZc63tf5PqlSj0o+PlV83B2i15D1MmyKrVwlyJYcC/63+bC2d+VPFrvVnmzJtb4D4Mvr5Q0egYgcyBsXmZWSJk2aYOvWrfm/L1myBBkZGWjdunWhnp+bm4vY2Fh07twZAHD58mX07dsXO3fuvGUbMTEx+O2331C3bl04HA6kpaWhc+fO2LdvX36b1NRUHD58GNOmTUPbtm0RExODSZMm4YcffkCPHj1c8noZu4G/cjFWxlCeaKfZ2YNUCr+279qys2+fNRs/MJpMcSbw7Jl3J+skqOVkqLcrienYMGmb7fWHZlt3Lfp+V056bnclxxJOROs9KTlm7tG1a1dYrVYsW7Ysf52iKIV+foUKFZCRkQGj0Qij0Yh27drdlhwDwNixY5Gamgqj0YgDBw6gYcOGtyTHQF4JxqxZswAAFosFQghIklSkeBgrrPLTPcJYOURESQD+LYSYZPrDNEw5p7wtG+RKVXpXCazwcAVxo5yA3UrIElmVsjvUmyXXit+/OYF9637JvZyQBgBL7TbHYtWiXnR3bMyzCCGwY8cOjB8/HnPnzkWVKlUQGBiIOXPmuGT7ERERfzv6xQ1Hjx4FALRs2RIAMHr0aDRr1gy1atXCtGnTXBILYzcT3JvEWPlxfRzZLlKA9BbZqZOhmcFesWPFQEMzAyQ9X1C6IXH8Oe2lT0dL9SJquzsUl9GcGk7/koD9G39VYqNPyTpf3X5LjnUJgG+4p7h8Cg8PTyijU02fi4uLu9/dcTDvxj3IjJUjlPeNOBpAtBCiSu7vuU+ZT5tfIAeFB7cMdoZ0CAkwhBsgdGW287RQhCyorMymd/lcGn7ZfNh+YPMhOznpotVs+5g02qBa7Rnujo0xxjwVJ8iMlVNElA5gOYDlQoia2YeyB+aezH0eGuoHtw5GyMMhfoGNAiGkcpgsy4DNbHN3FMVCRLiUkIZju086f/3qqPnaxSwCsMZuta8iolh3x8cYY96AE2TGGK6PbfshgA+FEHWzfskanPNbzmgIhIa0C5EqtK/gG9AgoPwkyxJItXhPD7KmaTAeu4Dfd5+0H955zGbJtdoAbFEVdTOAn6+P/sAYY6yQOEFmjN2CiIwA3gfwvhCiYeZPmUOzYrKeEbKoHBwZLAc1D/INbBwIOaAM3+CnE8Jq9uwE2W5zIP7gOfz+zQnr77tjCUTpdpt9g0N1bgHwO/ENJuwuTCZTcnh4uLvDcDmTyZTs7hiY9+MEmTF2R0QUD2A6gOlCiCaZP2f2zjmS85Rm01r61vS1BrcKDjQ0Ncj+9fwh5DLUu6wj2BTPKrEgIlw8fRmnDsTT8e/ico3HL/j5+OnPWs3W/2hO2k5EZ90dI/MuycnJXd0dA2OeihNkxlihENEfAP4A8IEQwt+abI2yXbI9nrEnoy85qGZAgwCrobkhKLBRoPCv4+UJsx6Szc09yDdqieMPnsMfP50xnf01UQYhm4DdNrPtawA/2m32TLcGyRhjZRQnyIyxIiMiC4C9APYKIcYDqGw+be5kSbL0Sv86vQc5qIZ/PX9rUPOgIP96/sKvlh90wd7z50b4SMJqshKAUsvyTdfMOH8yBcYTF7SEQ0mmpGPJeiLkSpKItuRavwXwExGdL614GGOsPPOeMxZjzFP9D8BQItoCYAsACCEqK/FKlMVo6S7ppQ6aTWso9AJ+YX6qfwP/AP/a/nq/Wn7wqeEDSed54y9L/hKUHKsGoEQKrZUcC5JPpsB4IoUSDieZjMcvSFaTVfIN8I2zWdSfHDbHQQCHbk6IhRDdhBATiOjlkoiJMcbYnzhBZowVmxCiKoDGAC7cvJ6IMgBsv75ACCHIQbWUs0pzJUGJkAPk9qRRC03VqvlU8lH86vjJAfUDAv1q+cGvlh90Fdz7p0nyk2DJtdzTTW6aU8O11CxcSUzHlcR0pMZftl48c9mWlpShs5ptOr9A3zOq1f6z3Wr/BcARAOfsNrP2N5s0AnhKCPEK34DHGGMlixNkxti96IJCDCN2PaFLvr7surFeCBGgpqnhapraIvd4bmtJL7XTbFpDAHo5SLbqQ/SavpJe9qni46+/Ty/rKuqgr6iHvqIeugq6EqtzlgNkWEy2O26ciGDJtSI7LQfZabnIvpKD7LQcZF7Otl9JTLdcPpeGrCs5AXofXbbOR5fosDtP2My2EwDOAIgHkGzKNDuLGFYiAAeAhte3wxhjrITwVNOMsWITQqwE8AcRfeTCbQoAwQBCb1pqSn5SA6ET9UAI0+xaVbJTkOQnWXUVdHb9fXroQnR6yUeSJL0kCx8hSz6SJHQCQi8g6SQI/fWf9RJurBeyADkJpBI0uway5/2b81sO9Bk6eqhfSzJnWVQlW3Eo2RYtJyOXctJNsjlb8ZOEcOj89NdkWboC4KJqtSfZrfZkAOeRl8AmEJHiquNy/dh8CuAwES1z5XYZY4zdihNkxlixCSESAPQnopNu2LcOQDX8mURXBuB7ffGDgK+QhUHIIgASAoUk/CHgD8APgP/1dj4AVAAWABYQzESkkJNU4RC+mlOLBZALIOf6kg7gEoBLRGQu3VcMCCGGA+hHRE+X9r4ZY6w84QSZMVYsQog6AA4BqM41saVDCBEK4DiAqkT0d/XKjDHG7oHn3T7OGPMWXQFEc3JceojoIoAMAM3dHQtjjJVlnCAzxoqrG4BodwdRDkUj78sJY4yxEsIlFoVUu3btaIPBUNvdcbiKyWRK5mlGWXFdv5EuFUAHIkp0dzzliRDiaQD/JKI+7o6FeTdvP6/xeYyVJB7mrZAMBkPtuLi4Bu6Ow1XCw8PdHQLzbo0A2AAkuTuQcmgfgNVCCD0R2d0dDPNe3n5e4/MYK0lcYsEYK45uAH7g+uPSd30SlkQArd0dC2OMlVWcIDPGiqMrgB/cHUQ5xnXIjDFWgjhBvgcGgwFGoxH+/v6IiIhAeHg4xowZA03TYDQaIYTAlClT8ttnZGRAr9fjxRdfvG1bmZmZ6N+/P5o3b462bdsiNja2wH0SESZNmoSGDRuicePGWLRoEQBg69ataNKkCaKionD16lUAwLlz5zB48OASeOWsPBNCyAA6A/jRzaGUZ5wgM5eTZRkRERFo0aIFWrVqhV9++eWWxxcuXAg/Pz9kZ2ffcRvJycno2bMnGjdujPDwcBiNxgLbbd68GeHh4WjSpAmGDh0KADhz5gwiIyPRokULxMTEAAAcDge6d+8ORXHpnDuM3R0R8VKIpXHjxgn0F4GBgZSUlERNmjQhIiK73U5RUVG0detWSkpKovr161NERER++6VLl1KLFi1o3Lhxf90UvfbaazR9+nQiIjp16hR17dr1tjZERJ9++imNGDGCnE4nERFduXKFiIjat29POTk5tHLlSlq0aBEREQ0ePJji4+ML3M711+P248qL9y0AIpE3e57bYymvC/JmGswF4OfuWHjx3uWv57XAwMD8n3fv3k2dOnW6+WFq06YNdezYkT777DO6k0ceeYT27t1LRES5ublkNptvaxMfH08RERF07do1IvrzPDZ+/Hjau3cvxcfH04ABA4iIaNGiRbRmzZoC98XnMV5KcuEeZBfS6XR4+OGHkZCQAADw9/dH48aNceTIEQDAF198gYEDBxb43Li4OHTr1g0A0KhRIxiNRly5cuW2dsuWLcPUqVMhSXn/dVWrVgUASJIEm80GRVGg1+uxf/9+1KhRAw888IDLXycr97qByyvciohyAMQCeNjdsbCyKScnBxUrVsz//dy5czCZTJg9ezY2btxY4HPi4uLgcDjQo0cPAHlXWQMCAm5r98knn2DcuHH5279xHtPr9bBYLPnnsaysLHz99dcYOXKkq18eY3fFo1i4kKIo+OGHHzBz5sz8dYMHD8amTZtQvXp1yLKMmjVrIjU19bbntmjRAtu2bUPHjh1x6NAhnD9/HikpKahWrdot7c6dO4cvvvgC27dvR5UqVbBo0SI88MADmDZtGnr16oWaNWvi888/x8CBA7Fp06a7xiyEWAvAa+9iZm7REMDz7g6CIRrAh0KIc+4OhHkVE4BRVMBMjBaLBREREbBarbh06RKio/8c5nzjxo0YMmQIoqKicObMGaSlpeUntjfEx8cjJCQEAwYMQFJSErp37473338fsizf1g4AOnToAKfTienTp+PRRx/FuHHjMHLkSNhsNqxYsQIzZ87EpEmTkDeqJGOlixNkFzh37hwiIiIghMCTTz6Jxx57LL/u6tFHH8WUKVNQrVo1DBo06I7bePPNN/HKK68gIiICzZo1Q8uWLaHT3f7fY7PZ4OfnhyNHjmDbtm149tlnsX//fvTo0SP/W/vatWvRu3dvnDlzBvPmzUPFihXx0UcfFfhNHsB7ACrf+1Fg5YgTeVNMM/eaD+CIu4NgXscEoMDRZ/z9/XHs2DEAQExMDEaOHInY2FgIIbBp0yZs374dkiRhwIAB+PLLLzFu3Lhbnu9wOLB//34cPXoUtWvXxqBBg7BmzRqMHj36tnZnz57Fvn37kJKSgqioKMTGxqJ27drYt28fACAhIQGpqalo1KgRRowYAVVVMWvWLDRs2NDlB4SxgnCC7AINGjTI/6PyVz4+PoiMjMT8+fPxxx9/4Ouvvy6wXXBwMD777DMAeXXh9erVQ7169W5rFxYWhqeeegoA0L9/fzzzzDO3PK4oCtauXYs9e/agZ8+e+Oqrr7BhwwasX78ezz9/e6cfEZ0uymtljHkGIroGYLu742BlU/v27ZGRkYH09HRcvnwZZ8+eze+EUVUV9evXvy1BDgsLQ8uWLVG/fn0AQL9+/XDw4MHbEuSwsDC0a9cOer0e9erVw4MPPoizZ8+iTZs2+W0mTZqE2bNnY9GiRRg2bBjq1q2LGTNmYP369SX8yhnLwzXIpWDChAmYM2cOKlWqdMc2WVlZUFUVALBq1Sp06tQJwcHBt7Xr169f/mWvn3766bZv03PnzsUrr7ySX8slhIAkSXwHMGOMsUI7ffo0nE4nKlWqhI0bN2L69OkwGo0wGo1ITU3FxYsXcf78+Vue06ZNG2RmZiI9PR0AEB0dXeBkHv369cOPP+YNgpORkYH4+Pj8pBrIO7eFhobigQcegKIokCQJsizzeYyVKu5BLiaHwwFfX99CtW3SpAmaNGnyt21OnTqFkSNHQpZlhIeHY/Xq1fmP9e7dG6tWrULNmjXx5ptvYtiwYVi4cCEMBgNWrVqV3y41NRVHjhzB9OnTAeQl5u3atUNISAh27NhR5NfIGGOs/LhRgwzkXclcu3YtZFnGpk2b8O23397Stn///ti0aRPeeOON/HWyLGPevHno1q0biAiRkZH5Vy6nTp2K1q1bo2/fvujVqxf27t2L8PBwyLKMDz74IL8DiYgwe/ZsbN68GQDwwgsvYNiwYXA4HFi2bFkpHAXG8ggingirMMLDwxNunpLz+PHjeP7553HokHeWYoaHh5+Li4u7391xMMYYc4+/nte8DZ/HWEniEotiWL58OYYMGYLZs2e7OxTGGGOMMeZiXGJRDGPGjMGYMWPcHQZjjDHGGCsB3IPMGGOMMcbYTbgHuZBMJlNyQXfjeiuTyZTs7hgYY4y5j7ef1/g8xkoS36THGGOMMcbYTbjEgjHGGGOMsZtwgswYY4wxxthNOEFmjDHGGGPsJpwgM8YYY4wxdpP/By7eksGRUFimAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = plf.plot_pie(w=w, title='Sharpe FM Mean Variance', others=0.05, nrow=25, cmap = \"tab20\",\n",
    "                  height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3.4 Calculate efficient frontier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.0033%</td>\n",
       "      <td>4.4251%</td>\n",
       "      <td>3.6050%</td>\n",
       "      <td>8.4993%</td>\n",
       "      <td>5.0116%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>4.4407%</td>\n",
       "      <td>...</td>\n",
       "      <td>12.0995%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>13.3267%</td>\n",
       "      <td>1.0159%</td>\n",
       "      <td>8.1901%</td>\n",
       "      <td>4.0879%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>10.3183%</td>\n",
       "      <td>1.6737%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.0467%</td>\n",
       "      <td>4.4741%</td>\n",
       "      <td>2.0057%</td>\n",
       "      <td>3.3537%</td>\n",
       "      <td>10.4704%</td>\n",
       "      <td>3.7214%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>3.0027%</td>\n",
       "      <td>...</td>\n",
       "      <td>13.5251%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>9.1906%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.6453%</td>\n",
       "      <td>4.6732%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>10.9803%</td>\n",
       "      <td>2.0507%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.7410%</td>\n",
       "      <td>4.7954%</td>\n",
       "      <td>0.7903%</td>\n",
       "      <td>2.9694%</td>\n",
       "      <td>11.5752%</td>\n",
       "      <td>3.1987%</td>\n",
       "      <td>0.3923%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.1218%</td>\n",
       "      <td>...</td>\n",
       "      <td>14.2351%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>7.3206%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>6.0850%</td>\n",
       "      <td>4.8025%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>11.4548%</td>\n",
       "      <td>2.1781%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.2663%</td>\n",
       "      <td>4.9910%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.6373%</td>\n",
       "      <td>12.4530%</td>\n",
       "      <td>2.7844%</td>\n",
       "      <td>0.8029%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>1.4005%</td>\n",
       "      <td>...</td>\n",
       "      <td>14.7948%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.8320%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.6384%</td>\n",
       "      <td>4.8920%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>11.8321%</td>\n",
       "      <td>2.2620%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.7309%</td>\n",
       "      <td>5.0078%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>2.2236%</td>\n",
       "      <td>13.3382%</td>\n",
       "      <td>2.3698%</td>\n",
       "      <td>1.1680%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.6178%</td>\n",
       "      <td>...</td>\n",
       "      <td>15.3286%</td>\n",
       "      <td>0.0003%</td>\n",
       "      <td>4.2963%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>5.1940%</td>\n",
       "      <td>4.9446%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>0.0000%</td>\n",
       "      <td>12.2137%</td>\n",
       "      <td>2.3335%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      APA      BA     BAX     BMY   CMCSA      CNP     CPB      DE     HPQ  \\\n",
       "0 0.0000% 0.0000% 2.0033% 4.4251% 3.6050%  8.4993% 5.0116% 0.0000% 0.0000%   \n",
       "1 0.0000% 1.0467% 4.4741% 2.0057% 3.3537% 10.4704% 3.7214% 0.0000% 0.0000%   \n",
       "2 0.0000% 1.7410% 4.7954% 0.7903% 2.9694% 11.5752% 3.1987% 0.3923% 0.0000%   \n",
       "3 0.0000% 2.2663% 4.9910% 0.0000% 2.6373% 12.4530% 2.7844% 0.8029% 0.0000%   \n",
       "4 0.0000% 2.7309% 5.0078% 0.0000% 2.2236% 13.3382% 2.3698% 1.1680% 0.0000%   \n",
       "\n",
       "      JCI  ...       NI    PCAR      PSA     SEE       T     TGT     TMO  \\\n",
       "0 4.4407%  ... 12.0995% 0.0000% 13.3267% 1.0159% 8.1901% 4.0879% 0.0000%   \n",
       "1 3.0027%  ... 13.5251% 0.0000%  9.1906% 0.0000% 6.6453% 4.6732% 0.0000%   \n",
       "2 2.1218%  ... 14.2351% 0.0000%  7.3206% 0.0000% 6.0850% 4.8025% 0.0000%   \n",
       "3 1.4005%  ... 14.7948% 0.0000%  5.8320% 0.0000% 5.6384% 4.8920% 0.0000%   \n",
       "4 0.6178%  ... 15.3286% 0.0003%  4.2963% 0.0000% 5.1940% 4.9446% 0.0000%   \n",
       "\n",
       "      TXT       VZ    ZION  \n",
       "0 0.0000% 10.3183% 1.6737%  \n",
       "1 0.0000% 10.9803% 2.0507%  \n",
       "2 0.0000% 11.4548% 2.1781%  \n",
       "3 0.0000% 11.8321% 2.2620%  \n",
       "4 0.0000% 12.2137% 2.3335%  \n",
       "\n",
       "[5 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "points = 50 # Number of points of the frontier\n",
    "\n",
    "frontier = port.efficient_frontier(model=model, rm=rm, points=points, rf=rf, hist=hist)\n",
    "\n",
    "display(frontier.T.head())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting efficient frontier composition\n",
    "\n",
    "ax = plf.plot_frontier(w_frontier=frontier, mu=mu, cov=cov, returns=returns, rm=rm,\n",
    "                       rf=rf, alpha=0.05, cmap='viridis', w=w, label=label,\n",
    "                       marker='*', s=16, c='r', height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting efficient frontier composition\n",
    "\n",
    "ax = plf.plot_frontier_area(w_frontier=frontier, cmap=\"tab20\", height=6, width=10, ax=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>APA</th>\n",
       "      <th>BA</th>\n",
       "      <th>BAX</th>\n",
       "      <th>BMY</th>\n",
       "      <th>CMCSA</th>\n",
       "      <th>CNP</th>\n",
       "      <th>CPB</th>\n",
       "      <th>DE</th>\n",
       "      <th>HPQ</th>\n",
       "      <th>JCI</th>\n",
       "      <th>...</th>\n",
       "      <th>NI</th>\n",
       "      <th>PCAR</th>\n",
       "      <th>PSA</th>\n",
       "      <th>SEE</th>\n",
       "      <th>T</th>\n",
       "      <th>TGT</th>\n",
       "      <th>TMO</th>\n",
       "      <th>TXT</th>\n",
       "      <th>VZ</th>\n",
       "      <th>ZION</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>-0.6212%</td>\n",
       "      <td>0.0684%</td>\n",
       "      <td>0.7015%</td>\n",
       "      <td>0.1909%</td>\n",
       "      <td>0.4771%</td>\n",
       "      <td>0.9282%</td>\n",
       "      <td>0.9912%</td>\n",
       "      <td>0.1761%</td>\n",
       "      <td>0.2253%</td>\n",
       "      <td>0.1002%</td>\n",
       "      <td>...</td>\n",
       "      <td>1.1711%</td>\n",
       "      <td>-0.1121%</td>\n",
       "      <td>0.8782%</td>\n",
       "      <td>0.3502%</td>\n",
       "      <td>0.6936%</td>\n",
       "      <td>0.1709%</td>\n",
       "      <td>0.5662%</td>\n",
       "      <td>-0.2137%</td>\n",
       "      <td>0.9752%</td>\n",
       "      <td>-0.3551%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>-2.8766%</td>\n",
       "      <td>-1.2757%</td>\n",
       "      <td>-0.6189%</td>\n",
       "      <td>-0.9938%</td>\n",
       "      <td>-0.8928%</td>\n",
       "      <td>-0.5878%</td>\n",
       "      <td>-0.3663%</td>\n",
       "      <td>-1.6301%</td>\n",
       "      <td>-2.2665%</td>\n",
       "      <td>-1.2376%</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0189%</td>\n",
       "      <td>-2.1654%</td>\n",
       "      <td>-0.0371%</td>\n",
       "      <td>-1.3275%</td>\n",
       "      <td>-1.3762%</td>\n",
       "      <td>-1.3348%</td>\n",
       "      <td>-1.0117%</td>\n",
       "      <td>-1.8914%</td>\n",
       "      <td>-0.9195%</td>\n",
       "      <td>-2.5118%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>-2.6603%</td>\n",
       "      <td>-2.6676%</td>\n",
       "      <td>-1.9233%</td>\n",
       "      <td>-1.9592%</td>\n",
       "      <td>-1.9764%</td>\n",
       "      <td>-1.2059%</td>\n",
       "      <td>-0.9113%</td>\n",
       "      <td>-2.3282%</td>\n",
       "      <td>-2.6834%</td>\n",
       "      <td>-1.8334%</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.8785%</td>\n",
       "      <td>-2.6708%</td>\n",
       "      <td>-1.0103%</td>\n",
       "      <td>-1.9321%</td>\n",
       "      <td>-1.3740%</td>\n",
       "      <td>-1.5868%</td>\n",
       "      <td>-2.5264%</td>\n",
       "      <td>-2.5834%</td>\n",
       "      <td>-1.1151%</td>\n",
       "      <td>-2.2578%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>-1.6385%</td>\n",
       "      <td>-1.2856%</td>\n",
       "      <td>-1.0494%</td>\n",
       "      <td>-0.9502%</td>\n",
       "      <td>-1.0563%</td>\n",
       "      <td>-1.0629%</td>\n",
       "      <td>-0.9909%</td>\n",
       "      <td>-1.2707%</td>\n",
       "      <td>-1.3400%</td>\n",
       "      <td>-1.0335%</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.9977%</td>\n",
       "      <td>-1.3239%</td>\n",
       "      <td>-0.9137%</td>\n",
       "      <td>-1.2706%</td>\n",
       "      <td>-0.9455%</td>\n",
       "      <td>-0.7750%</td>\n",
       "      <td>-1.2535%</td>\n",
       "      <td>-1.3153%</td>\n",
       "      <td>-0.9453%</td>\n",
       "      <td>-1.1138%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-11</th>\n",
       "      <td>-2.1923%</td>\n",
       "      <td>-0.0564%</td>\n",
       "      <td>0.3346%</td>\n",
       "      <td>0.0940%</td>\n",
       "      <td>0.1556%</td>\n",
       "      <td>-0.0891%</td>\n",
       "      <td>-0.0995%</td>\n",
       "      <td>-0.6705%</td>\n",
       "      <td>-0.7531%</td>\n",
       "      <td>-0.5542%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2116%</td>\n",
       "      <td>-0.7575%</td>\n",
       "      <td>0.3163%</td>\n",
       "      <td>-0.5897%</td>\n",
       "      <td>-0.4351%</td>\n",
       "      <td>-0.4203%</td>\n",
       "      <td>0.3350%</td>\n",
       "      <td>-0.8259%</td>\n",
       "      <td>-0.1978%</td>\n",
       "      <td>-1.4465%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-23</th>\n",
       "      <td>0.6973%</td>\n",
       "      <td>0.2477%</td>\n",
       "      <td>-0.4231%</td>\n",
       "      <td>-0.0181%</td>\n",
       "      <td>-0.1988%</td>\n",
       "      <td>-0.5601%</td>\n",
       "      <td>-0.7398%</td>\n",
       "      <td>0.1196%</td>\n",
       "      <td>0.1322%</td>\n",
       "      <td>0.0606%</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.8528%</td>\n",
       "      <td>0.4842%</td>\n",
       "      <td>-0.6862%</td>\n",
       "      <td>-0.1714%</td>\n",
       "      <td>-0.3265%</td>\n",
       "      <td>0.1146%</td>\n",
       "      <td>-0.2473%</td>\n",
       "      <td>0.3183%</td>\n",
       "      <td>-0.5772%</td>\n",
       "      <td>0.6377%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-24</th>\n",
       "      <td>-0.1965%</td>\n",
       "      <td>0.0027%</td>\n",
       "      <td>0.1506%</td>\n",
       "      <td>-0.0503%</td>\n",
       "      <td>0.0391%</td>\n",
       "      <td>0.0930%</td>\n",
       "      <td>0.1006%</td>\n",
       "      <td>0.0557%</td>\n",
       "      <td>-0.0071%</td>\n",
       "      <td>0.0206%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1566%</td>\n",
       "      <td>-0.1185%</td>\n",
       "      <td>0.0728%</td>\n",
       "      <td>-0.0238%</td>\n",
       "      <td>0.0130%</td>\n",
       "      <td>0.0305%</td>\n",
       "      <td>0.0896%</td>\n",
       "      <td>-0.0882%</td>\n",
       "      <td>0.0777%</td>\n",
       "      <td>-0.0760%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-26</th>\n",
       "      <td>0.0838%</td>\n",
       "      <td>0.3934%</td>\n",
       "      <td>0.3568%</td>\n",
       "      <td>0.2717%</td>\n",
       "      <td>0.3344%</td>\n",
       "      <td>0.2787%</td>\n",
       "      <td>0.1966%</td>\n",
       "      <td>0.3099%</td>\n",
       "      <td>0.3688%</td>\n",
       "      <td>0.2053%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.2526%</td>\n",
       "      <td>0.3537%</td>\n",
       "      <td>0.2094%</td>\n",
       "      <td>0.2125%</td>\n",
       "      <td>0.2702%</td>\n",
       "      <td>0.2577%</td>\n",
       "      <td>0.4285%</td>\n",
       "      <td>0.2202%</td>\n",
       "      <td>0.2812%</td>\n",
       "      <td>0.2258%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-27</th>\n",
       "      <td>-0.7229%</td>\n",
       "      <td>-0.0384%</td>\n",
       "      <td>0.2654%</td>\n",
       "      <td>-0.0101%</td>\n",
       "      <td>0.1392%</td>\n",
       "      <td>0.3665%</td>\n",
       "      <td>0.3917%</td>\n",
       "      <td>-0.1304%</td>\n",
       "      <td>-0.2432%</td>\n",
       "      <td>-0.1203%</td>\n",
       "      <td>...</td>\n",
       "      <td>0.5586%</td>\n",
       "      <td>-0.2998%</td>\n",
       "      <td>0.4284%</td>\n",
       "      <td>-0.0293%</td>\n",
       "      <td>0.0814%</td>\n",
       "      <td>-0.1122%</td>\n",
       "      <td>0.1726%</td>\n",
       "      <td>-0.3568%</td>\n",
       "      <td>0.2897%</td>\n",
       "      <td>-0.5416%</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-12-30</th>\n",
       "      <td>-0.0583%</td>\n",
       "      <td>0.0476%</td>\n",
       "      <td>0.0293%</td>\n",
       "      <td>-0.0261%</td>\n",
       "      <td>0.0082%</td>\n",
       "      <td>0.0074%</td>\n",
       "      <td>-0.0320%</td>\n",
       "      <td>0.0439%</td>\n",
       "      <td>0.0207%</td>\n",
       "      <td>0.0054%</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.0045%</td>\n",
       "      <td>0.0257%</td>\n",
       "      <td>-0.0422%</td>\n",
       "      <td>-0.0464%</td>\n",
       "      <td>-0.0049%</td>\n",
       "      <td>0.0473%</td>\n",
       "      <td>0.0243%</td>\n",
       "      <td>-0.0347%</td>\n",
       "      <td>0.0028%</td>\n",
       "      <td>0.0447%</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1004 rows × 25 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                APA       BA      BAX      BMY    CMCSA      CNP      CPB  \\\n",
       "2016-01-05 -0.6212%  0.0684%  0.7015%  0.1909%  0.4771%  0.9282%  0.9912%   \n",
       "2016-01-06 -2.8766% -1.2757% -0.6189% -0.9938% -0.8928% -0.5878% -0.3663%   \n",
       "2016-01-07 -2.6603% -2.6676% -1.9233% -1.9592% -1.9764% -1.2059% -0.9113%   \n",
       "2016-01-08 -1.6385% -1.2856% -1.0494% -0.9502% -1.0563% -1.0629% -0.9909%   \n",
       "2016-01-11 -2.1923% -0.0564%  0.3346%  0.0940%  0.1556% -0.0891% -0.0995%   \n",
       "...             ...      ...      ...      ...      ...      ...      ...   \n",
       "2019-12-23  0.6973%  0.2477% -0.4231% -0.0181% -0.1988% -0.5601% -0.7398%   \n",
       "2019-12-24 -0.1965%  0.0027%  0.1506% -0.0503%  0.0391%  0.0930%  0.1006%   \n",
       "2019-12-26  0.0838%  0.3934%  0.3568%  0.2717%  0.3344%  0.2787%  0.1966%   \n",
       "2019-12-27 -0.7229% -0.0384%  0.2654% -0.0101%  0.1392%  0.3665%  0.3917%   \n",
       "2019-12-30 -0.0583%  0.0476%  0.0293% -0.0261%  0.0082%  0.0074% -0.0320%   \n",
       "\n",
       "                 DE      HPQ      JCI  ...       NI     PCAR      PSA  \\\n",
       "2016-01-05  0.1761%  0.2253%  0.1002%  ...  1.1711% -0.1121%  0.8782%   \n",
       "2016-01-06 -1.6301% -2.2665% -1.2376%  ... -0.0189% -2.1654% -0.0371%   \n",
       "2016-01-07 -2.3282% -2.6834% -1.8334%  ... -0.8785% -2.6708% -1.0103%   \n",
       "2016-01-08 -1.2707% -1.3400% -1.0335%  ... -0.9977% -1.3239% -0.9137%   \n",
       "2016-01-11 -0.6705% -0.7531% -0.5542%  ...  0.2116% -0.7575%  0.3163%   \n",
       "...             ...      ...      ...  ...      ...      ...      ...   \n",
       "2019-12-23  0.1196%  0.1322%  0.0606%  ... -0.8528%  0.4842% -0.6862%   \n",
       "2019-12-24  0.0557% -0.0071%  0.0206%  ...  0.1566% -0.1185%  0.0728%   \n",
       "2019-12-26  0.3099%  0.3688%  0.2053%  ...  0.2526%  0.3537%  0.2094%   \n",
       "2019-12-27 -0.1304% -0.2432% -0.1203%  ...  0.5586% -0.2998%  0.4284%   \n",
       "2019-12-30  0.0439%  0.0207%  0.0054%  ... -0.0045%  0.0257% -0.0422%   \n",
       "\n",
       "                SEE        T      TGT      TMO      TXT       VZ     ZION  \n",
       "2016-01-05  0.3502%  0.6936%  0.1709%  0.5662% -0.2137%  0.9752% -0.3551%  \n",
       "2016-01-06 -1.3275% -1.3762% -1.3348% -1.0117% -1.8914% -0.9195% -2.5118%  \n",
       "2016-01-07 -1.9321% -1.3740% -1.5868% -2.5264% -2.5834% -1.1151% -2.2578%  \n",
       "2016-01-08 -1.2706% -0.9455% -0.7750% -1.2535% -1.3153% -0.9453% -1.1138%  \n",
       "2016-01-11 -0.5897% -0.4351% -0.4203%  0.3350% -0.8259% -0.1978% -1.4465%  \n",
       "...             ...      ...      ...      ...      ...      ...      ...  \n",
       "2019-12-23 -0.1714% -0.3265%  0.1146% -0.2473%  0.3183% -0.5772%  0.6377%  \n",
       "2019-12-24 -0.0238%  0.0130%  0.0305%  0.0896% -0.0882%  0.0777% -0.0760%  \n",
       "2019-12-26  0.2125%  0.2702%  0.2577%  0.4285%  0.2202%  0.2812%  0.2258%  \n",
       "2019-12-27 -0.0293%  0.0814% -0.1122%  0.1726% -0.3568%  0.2897% -0.5416%  \n",
       "2019-12-30 -0.0464% -0.0049%  0.0473%  0.0243% -0.0347%  0.0028%  0.0447%  \n",
       "\n",
       "[1004 rows x 25 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "display(returns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Estimating Portfolios Using Risk Factors with Other Risk Measures\n",
    "\n",
    "In this part I will calculate optimal portfolios for several risk measures. I will find the portfolios that maximize the risk adjusted return for all available risk measures.\n",
    "\n",
    "### 4.1 Calculate Optimal Portfolios for Several Risk Measures.\n",
    "\n",
    "I will mantain the constraints on risk factors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Risk Measures available:\n",
    "#\n",
    "# 'MV': Standard Deviation.\n",
    "# 'MAD': Mean Absolute Deviation.\n",
    "# 'MSV': Semi Standard Deviation.\n",
    "# 'FLPM': First Lower Partial Moment (Omega Ratio).\n",
    "# 'SLPM': Second Lower Partial Moment (Sortino Ratio).\n",
    "# 'CVaR': Conditional Value at Risk.\n",
    "# 'EVaR': Entropic Value at Risk.\n",
    "# 'WR': Worst Realization (Minimax)\n",
    "# 'MDD': Maximum Drawdown of uncompounded cumulative returns (Calmar Ratio).\n",
    "# 'ADD': Average Drawdown of uncompounded cumulative returns.\n",
    "# 'CDaR': Conditional Drawdown at Risk of uncompounded cumulative returns.\n",
    "# 'EDaR': Entropic Drawdown at Risk of uncompounded cumulative returns.\n",
    "# 'UCI': Ulcer Index of uncompounded cumulative returns.\n",
    "\n",
    "# port.reset_linear_constraints() # To reset linear constraints (factor constraints)\n",
    "\n",
    "rms = ['MV', 'MAD', 'MSV', 'FLPM', 'SLPM', 'CVaR',\n",
    "       'EVaR', 'WR', 'MDD', 'ADD', 'CDaR', 'UCI', 'EDaR']\n",
    "\n",
    "w_s = pd.DataFrame([])\n",
    "\n",
    "# When we use hist = True the risk measures all calculated\n",
    "# using historical returns, while when hist = False the\n",
    "# risk measures are calculated using the expected returns \n",
    "#  based on risk factor model: R = a + B * F\n",
    "\n",
    "hist = False\n",
    "for i in rms:\n",
    "    w = port.optimization(model=model, rm=i, obj=obj, rf=rf, l=l, hist=hist)\n",
    "    w_s = pd.concat([w_s, w], axis=1)\n",
    "    \n",
    "w_s.columns = rms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        }</style><table id=\"T_aa162_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MV</th>        <th class=\"col_heading level0 col1\" >MAD</th>        <th class=\"col_heading level0 col2\" >MSV</th>        <th class=\"col_heading level0 col3\" >FLPM</th>        <th class=\"col_heading level0 col4\" >SLPM</th>        <th class=\"col_heading level0 col5\" >CVaR</th>        <th class=\"col_heading level0 col6\" >EVaR</th>        <th class=\"col_heading level0 col7\" >WR</th>        <th class=\"col_heading level0 col8\" >MDD</th>        <th class=\"col_heading level0 col9\" >ADD</th>        <th class=\"col_heading level0 col10\" >CDaR</th>        <th class=\"col_heading level0 col11\" >UCI</th>        <th class=\"col_heading level0 col12\" >EDaR</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_aa162_level0_row0\" class=\"row_heading level0 row0\" >APA</th>\n",
       "                        <td id=\"T_aa162_row0_col0\" class=\"data row0 col0\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row0_col1\" class=\"data row0 col1\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row0_col2\" class=\"data row0 col2\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row0_col3\" class=\"data row0 col3\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row0_col4\" class=\"data row0 col4\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row0_col5\" class=\"data row0 col5\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row0_col6\" class=\"data row0 col6\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row0_col7\" class=\"data row0 col7\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row0_col8\" class=\"data row0 col8\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row0_col9\" class=\"data row0 col9\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row0_col10\" class=\"data row0 col10\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row0_col11\" class=\"data row0 col11\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row0_col12\" class=\"data row0 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row1\" class=\"row_heading level0 row1\" >BA</th>\n",
       "                        <td id=\"T_aa162_row1_col0\" class=\"data row1 col0\" >6.59%</td>\n",
       "                        <td id=\"T_aa162_row1_col1\" class=\"data row1 col1\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row1_col2\" class=\"data row1 col2\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row1_col3\" class=\"data row1 col3\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row1_col4\" class=\"data row1 col4\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row1_col5\" class=\"data row1 col5\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row1_col6\" class=\"data row1 col6\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row1_col7\" class=\"data row1 col7\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row1_col8\" class=\"data row1 col8\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row1_col9\" class=\"data row1 col9\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row1_col10\" class=\"data row1 col10\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row1_col11\" class=\"data row1 col11\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row1_col12\" class=\"data row1 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row2\" class=\"row_heading level0 row2\" >BAX</th>\n",
       "                        <td id=\"T_aa162_row2_col0\" class=\"data row2 col0\" >1.46%</td>\n",
       "                        <td id=\"T_aa162_row2_col1\" class=\"data row2 col1\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row2_col2\" class=\"data row2 col2\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row2_col3\" class=\"data row2 col3\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row2_col4\" class=\"data row2 col4\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row2_col5\" class=\"data row2 col5\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row2_col6\" class=\"data row2 col6\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row2_col7\" class=\"data row2 col7\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row2_col8\" class=\"data row2 col8\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row2_col9\" class=\"data row2 col9\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row2_col10\" class=\"data row2 col10\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row2_col11\" class=\"data row2 col11\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row2_col12\" class=\"data row2 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row3\" class=\"row_heading level0 row3\" >BMY</th>\n",
       "                        <td id=\"T_aa162_row3_col0\" class=\"data row3 col0\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row3_col1\" class=\"data row3 col1\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row3_col2\" class=\"data row3 col2\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row3_col3\" class=\"data row3 col3\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row3_col4\" class=\"data row3 col4\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row3_col5\" class=\"data row3 col5\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row3_col6\" class=\"data row3 col6\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row3_col7\" class=\"data row3 col7\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row3_col8\" class=\"data row3 col8\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row3_col9\" class=\"data row3 col9\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row3_col10\" class=\"data row3 col10\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row3_col11\" class=\"data row3 col11\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row3_col12\" class=\"data row3 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row4\" class=\"row_heading level0 row4\" >CMCSA</th>\n",
       "                        <td id=\"T_aa162_row4_col0\" class=\"data row4 col0\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row4_col1\" class=\"data row4 col1\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row4_col2\" class=\"data row4 col2\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row4_col3\" class=\"data row4 col3\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row4_col4\" class=\"data row4 col4\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row4_col5\" class=\"data row4 col5\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row4_col6\" class=\"data row4 col6\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row4_col7\" class=\"data row4 col7\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row4_col8\" class=\"data row4 col8\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row4_col9\" class=\"data row4 col9\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row4_col10\" class=\"data row4 col10\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row4_col11\" class=\"data row4 col11\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row4_col12\" class=\"data row4 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row5\" class=\"row_heading level0 row5\" >CNP</th>\n",
       "                        <td id=\"T_aa162_row5_col0\" class=\"data row5 col0\" >17.55%</td>\n",
       "                        <td id=\"T_aa162_row5_col1\" class=\"data row5 col1\" >42.32%</td>\n",
       "                        <td id=\"T_aa162_row5_col2\" class=\"data row5 col2\" >53.62%</td>\n",
       "                        <td id=\"T_aa162_row5_col3\" class=\"data row5 col3\" >41.97%</td>\n",
       "                        <td id=\"T_aa162_row5_col4\" class=\"data row5 col4\" >53.62%</td>\n",
       "                        <td id=\"T_aa162_row5_col5\" class=\"data row5 col5\" >41.33%</td>\n",
       "                        <td id=\"T_aa162_row5_col6\" class=\"data row5 col6\" >53.62%</td>\n",
       "                        <td id=\"T_aa162_row5_col7\" class=\"data row5 col7\" >48.75%</td>\n",
       "                        <td id=\"T_aa162_row5_col8\" class=\"data row5 col8\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row5_col9\" class=\"data row5 col9\" >46.96%</td>\n",
       "                        <td id=\"T_aa162_row5_col10\" class=\"data row5 col10\" >23.97%</td>\n",
       "                        <td id=\"T_aa162_row5_col11\" class=\"data row5 col11\" >46.45%</td>\n",
       "                        <td id=\"T_aa162_row5_col12\" class=\"data row5 col12\" >43.15%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row6\" class=\"row_heading level0 row6\" >CPB</th>\n",
       "                        <td id=\"T_aa162_row6_col0\" class=\"data row6 col0\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row6_col1\" class=\"data row6 col1\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row6_col2\" class=\"data row6 col2\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row6_col3\" class=\"data row6 col3\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row6_col4\" class=\"data row6 col4\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row6_col5\" class=\"data row6 col5\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row6_col6\" class=\"data row6 col6\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row6_col7\" class=\"data row6 col7\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row6_col8\" class=\"data row6 col8\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row6_col9\" class=\"data row6 col9\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row6_col10\" class=\"data row6 col10\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row6_col11\" class=\"data row6 col11\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row6_col12\" class=\"data row6 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row7\" class=\"row_heading level0 row7\" >DE</th>\n",
       "                        <td id=\"T_aa162_row7_col0\" class=\"data row7 col0\" >4.10%</td>\n",
       "                        <td id=\"T_aa162_row7_col1\" class=\"data row7 col1\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row7_col2\" class=\"data row7 col2\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row7_col3\" class=\"data row7 col3\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row7_col4\" class=\"data row7 col4\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row7_col5\" class=\"data row7 col5\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row7_col6\" class=\"data row7 col6\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row7_col7\" class=\"data row7 col7\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row7_col8\" class=\"data row7 col8\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row7_col9\" class=\"data row7 col9\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row7_col10\" class=\"data row7 col10\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row7_col11\" class=\"data row7 col11\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row7_col12\" class=\"data row7 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row8\" class=\"row_heading level0 row8\" >HPQ</th>\n",
       "                        <td id=\"T_aa162_row8_col0\" class=\"data row8 col0\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row8_col1\" class=\"data row8 col1\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row8_col2\" class=\"data row8 col2\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row8_col3\" class=\"data row8 col3\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row8_col4\" class=\"data row8 col4\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row8_col5\" class=\"data row8 col5\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row8_col6\" class=\"data row8 col6\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row8_col7\" class=\"data row8 col7\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row8_col8\" class=\"data row8 col8\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row8_col9\" class=\"data row8 col9\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row8_col10\" class=\"data row8 col10\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row8_col11\" class=\"data row8 col11\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row8_col12\" class=\"data row8 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row9\" class=\"row_heading level0 row9\" >JCI</th>\n",
       "                        <td id=\"T_aa162_row9_col0\" class=\"data row9 col0\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row9_col1\" class=\"data row9 col1\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row9_col2\" class=\"data row9 col2\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row9_col3\" class=\"data row9 col3\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row9_col4\" class=\"data row9 col4\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row9_col5\" class=\"data row9 col5\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row9_col6\" class=\"data row9 col6\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row9_col7\" class=\"data row9 col7\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row9_col8\" class=\"data row9 col8\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row9_col9\" class=\"data row9 col9\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row9_col10\" class=\"data row9 col10\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row9_col11\" class=\"data row9 col11\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row9_col12\" class=\"data row9 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row10\" class=\"row_heading level0 row10\" >JPM</th>\n",
       "                        <td id=\"T_aa162_row10_col0\" class=\"data row10 col0\" >9.57%</td>\n",
       "                        <td id=\"T_aa162_row10_col1\" class=\"data row10 col1\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row10_col2\" class=\"data row10 col2\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row10_col3\" class=\"data row10 col3\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row10_col4\" class=\"data row10 col4\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row10_col5\" class=\"data row10 col5\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row10_col6\" class=\"data row10 col6\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row10_col7\" class=\"data row10 col7\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row10_col8\" class=\"data row10 col8\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row10_col9\" class=\"data row10 col9\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row10_col10\" class=\"data row10 col10\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row10_col11\" class=\"data row10 col11\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row10_col12\" class=\"data row10 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row11\" class=\"row_heading level0 row11\" >LUV</th>\n",
       "                        <td id=\"T_aa162_row11_col0\" class=\"data row11 col0\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row11_col1\" class=\"data row11 col1\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row11_col2\" class=\"data row11 col2\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row11_col3\" class=\"data row11 col3\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row11_col4\" class=\"data row11 col4\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row11_col5\" class=\"data row11 col5\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row11_col6\" class=\"data row11 col6\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row11_col7\" class=\"data row11 col7\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row11_col8\" class=\"data row11 col8\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row11_col9\" class=\"data row11 col9\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row11_col10\" class=\"data row11 col10\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row11_col11\" class=\"data row11 col11\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row11_col12\" class=\"data row11 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row12\" class=\"row_heading level0 row12\" >MMC</th>\n",
       "                        <td id=\"T_aa162_row12_col0\" class=\"data row12 col0\" >28.13%</td>\n",
       "                        <td id=\"T_aa162_row12_col1\" class=\"data row12 col1\" >23.48%</td>\n",
       "                        <td id=\"T_aa162_row12_col2\" class=\"data row12 col2\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row12_col3\" class=\"data row12 col3\" >24.20%</td>\n",
       "                        <td id=\"T_aa162_row12_col4\" class=\"data row12 col4\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row12_col5\" class=\"data row12 col5\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row12_col6\" class=\"data row12 col6\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row12_col7\" class=\"data row12 col7\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row12_col8\" class=\"data row12 col8\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row12_col9\" class=\"data row12 col9\" >13.82%</td>\n",
       "                        <td id=\"T_aa162_row12_col10\" class=\"data row12 col10\" >61.60%</td>\n",
       "                        <td id=\"T_aa162_row12_col11\" class=\"data row12 col11\" >14.89%</td>\n",
       "                        <td id=\"T_aa162_row12_col12\" class=\"data row12 col12\" >0.89%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row13\" class=\"row_heading level0 row13\" >MO</th>\n",
       "                        <td id=\"T_aa162_row13_col0\" class=\"data row13 col0\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row13_col1\" class=\"data row13 col1\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row13_col2\" class=\"data row13 col2\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row13_col3\" class=\"data row13 col3\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row13_col4\" class=\"data row13 col4\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row13_col5\" class=\"data row13 col5\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row13_col6\" class=\"data row13 col6\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row13_col7\" class=\"data row13 col7\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row13_col8\" class=\"data row13 col8\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row13_col9\" class=\"data row13 col9\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row13_col10\" class=\"data row13 col10\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row13_col11\" class=\"data row13 col11\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row13_col12\" class=\"data row13 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row14\" class=\"row_heading level0 row14\" >MSFT</th>\n",
       "                        <td id=\"T_aa162_row14_col0\" class=\"data row14 col0\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row14_col1\" class=\"data row14 col1\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row14_col2\" class=\"data row14 col2\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row14_col3\" class=\"data row14 col3\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row14_col4\" class=\"data row14 col4\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row14_col5\" class=\"data row14 col5\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row14_col6\" class=\"data row14 col6\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row14_col7\" class=\"data row14 col7\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row14_col8\" class=\"data row14 col8\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row14_col9\" class=\"data row14 col9\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row14_col10\" class=\"data row14 col10\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row14_col11\" class=\"data row14 col11\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row14_col12\" class=\"data row14 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row15\" class=\"row_heading level0 row15\" >NI</th>\n",
       "                        <td id=\"T_aa162_row15_col0\" class=\"data row15 col0\" >14.01%</td>\n",
       "                        <td id=\"T_aa162_row15_col1\" class=\"data row15 col1\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row15_col2\" class=\"data row15 col2\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row15_col3\" class=\"data row15 col3\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row15_col4\" class=\"data row15 col4\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row15_col5\" class=\"data row15 col5\" >14.79%</td>\n",
       "                        <td id=\"T_aa162_row15_col6\" class=\"data row15 col6\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row15_col7\" class=\"data row15 col7\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row15_col8\" class=\"data row15 col8\" >64.54%</td>\n",
       "                        <td id=\"T_aa162_row15_col9\" class=\"data row15 col9\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row15_col10\" class=\"data row15 col10\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row15_col11\" class=\"data row15 col11\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row15_col12\" class=\"data row15 col12\" >12.08%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row16\" class=\"row_heading level0 row16\" >PCAR</th>\n",
       "                        <td id=\"T_aa162_row16_col0\" class=\"data row16 col0\" >1.42%</td>\n",
       "                        <td id=\"T_aa162_row16_col1\" class=\"data row16 col1\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row16_col2\" class=\"data row16 col2\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row16_col3\" class=\"data row16 col3\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row16_col4\" class=\"data row16 col4\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row16_col5\" class=\"data row16 col5\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row16_col6\" class=\"data row16 col6\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row16_col7\" class=\"data row16 col7\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row16_col8\" class=\"data row16 col8\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row16_col9\" class=\"data row16 col9\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row16_col10\" class=\"data row16 col10\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row16_col11\" class=\"data row16 col11\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row16_col12\" class=\"data row16 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row17\" class=\"row_heading level0 row17\" >PSA</th>\n",
       "                        <td id=\"T_aa162_row17_col0\" class=\"data row17 col0\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row17_col1\" class=\"data row17 col1\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row17_col2\" class=\"data row17 col2\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row17_col3\" class=\"data row17 col3\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row17_col4\" class=\"data row17 col4\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row17_col5\" class=\"data row17 col5\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row17_col6\" class=\"data row17 col6\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row17_col7\" class=\"data row17 col7\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row17_col8\" class=\"data row17 col8\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row17_col9\" class=\"data row17 col9\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row17_col10\" class=\"data row17 col10\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row17_col11\" class=\"data row17 col11\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row17_col12\" class=\"data row17 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row18\" class=\"row_heading level0 row18\" >SEE</th>\n",
       "                        <td id=\"T_aa162_row18_col0\" class=\"data row18 col0\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row18_col1\" class=\"data row18 col1\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row18_col2\" class=\"data row18 col2\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row18_col3\" class=\"data row18 col3\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row18_col4\" class=\"data row18 col4\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row18_col5\" class=\"data row18 col5\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row18_col6\" class=\"data row18 col6\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row18_col7\" class=\"data row18 col7\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row18_col8\" class=\"data row18 col8\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row18_col9\" class=\"data row18 col9\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row18_col10\" class=\"data row18 col10\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row18_col11\" class=\"data row18 col11\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row18_col12\" class=\"data row18 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row19\" class=\"row_heading level0 row19\" >T</th>\n",
       "                        <td id=\"T_aa162_row19_col0\" class=\"data row19 col0\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row19_col1\" class=\"data row19 col1\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row19_col2\" class=\"data row19 col2\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row19_col3\" class=\"data row19 col3\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row19_col4\" class=\"data row19 col4\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row19_col5\" class=\"data row19 col5\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row19_col6\" class=\"data row19 col6\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row19_col7\" class=\"data row19 col7\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row19_col8\" class=\"data row19 col8\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row19_col9\" class=\"data row19 col9\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row19_col10\" class=\"data row19 col10\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row19_col11\" class=\"data row19 col11\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row19_col12\" class=\"data row19 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row20\" class=\"row_heading level0 row20\" >TGT</th>\n",
       "                        <td id=\"T_aa162_row20_col0\" class=\"data row20 col0\" >4.42%</td>\n",
       "                        <td id=\"T_aa162_row20_col1\" class=\"data row20 col1\" >34.20%</td>\n",
       "                        <td id=\"T_aa162_row20_col2\" class=\"data row20 col2\" >46.38%</td>\n",
       "                        <td id=\"T_aa162_row20_col3\" class=\"data row20 col3\" >33.83%</td>\n",
       "                        <td id=\"T_aa162_row20_col4\" class=\"data row20 col4\" >46.38%</td>\n",
       "                        <td id=\"T_aa162_row20_col5\" class=\"data row20 col5\" >43.88%</td>\n",
       "                        <td id=\"T_aa162_row20_col6\" class=\"data row20 col6\" >46.38%</td>\n",
       "                        <td id=\"T_aa162_row20_col7\" class=\"data row20 col7\" >39.77%</td>\n",
       "                        <td id=\"T_aa162_row20_col8\" class=\"data row20 col8\" >35.46%</td>\n",
       "                        <td id=\"T_aa162_row20_col9\" class=\"data row20 col9\" >39.21%</td>\n",
       "                        <td id=\"T_aa162_row20_col10\" class=\"data row20 col10\" >14.43%</td>\n",
       "                        <td id=\"T_aa162_row20_col11\" class=\"data row20 col11\" >38.66%</td>\n",
       "                        <td id=\"T_aa162_row20_col12\" class=\"data row20 col12\" >43.87%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row21\" class=\"row_heading level0 row21\" >TMO</th>\n",
       "                        <td id=\"T_aa162_row21_col0\" class=\"data row21 col0\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row21_col1\" class=\"data row21 col1\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row21_col2\" class=\"data row21 col2\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row21_col3\" class=\"data row21 col3\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row21_col4\" class=\"data row21 col4\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row21_col5\" class=\"data row21 col5\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row21_col6\" class=\"data row21 col6\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row21_col7\" class=\"data row21 col7\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row21_col8\" class=\"data row21 col8\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row21_col9\" class=\"data row21 col9\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row21_col10\" class=\"data row21 col10\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row21_col11\" class=\"data row21 col11\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row21_col12\" class=\"data row21 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row22\" class=\"row_heading level0 row22\" >TXT</th>\n",
       "                        <td id=\"T_aa162_row22_col0\" class=\"data row22 col0\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row22_col1\" class=\"data row22 col1\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row22_col2\" class=\"data row22 col2\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row22_col3\" class=\"data row22 col3\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row22_col4\" class=\"data row22 col4\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row22_col5\" class=\"data row22 col5\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row22_col6\" class=\"data row22 col6\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row22_col7\" class=\"data row22 col7\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row22_col8\" class=\"data row22 col8\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row22_col9\" class=\"data row22 col9\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row22_col10\" class=\"data row22 col10\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row22_col11\" class=\"data row22 col11\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row22_col12\" class=\"data row22 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row23\" class=\"row_heading level0 row23\" >VZ</th>\n",
       "                        <td id=\"T_aa162_row23_col0\" class=\"data row23 col0\" >10.64%</td>\n",
       "                        <td id=\"T_aa162_row23_col1\" class=\"data row23 col1\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row23_col2\" class=\"data row23 col2\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row23_col3\" class=\"data row23 col3\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row23_col4\" class=\"data row23 col4\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row23_col5\" class=\"data row23 col5\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row23_col6\" class=\"data row23 col6\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row23_col7\" class=\"data row23 col7\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row23_col8\" class=\"data row23 col8\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row23_col9\" class=\"data row23 col9\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row23_col10\" class=\"data row23 col10\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row23_col11\" class=\"data row23 col11\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row23_col12\" class=\"data row23 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_aa162_level0_row24\" class=\"row_heading level0 row24\" >ZION</th>\n",
       "                        <td id=\"T_aa162_row24_col0\" class=\"data row24 col0\" >2.12%</td>\n",
       "                        <td id=\"T_aa162_row24_col1\" class=\"data row24 col1\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row24_col2\" class=\"data row24 col2\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row24_col3\" class=\"data row24 col3\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row24_col4\" class=\"data row24 col4\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row24_col5\" class=\"data row24 col5\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row24_col6\" class=\"data row24 col6\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row24_col7\" class=\"data row24 col7\" >11.48%</td>\n",
       "                        <td id=\"T_aa162_row24_col8\" class=\"data row24 col8\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row24_col9\" class=\"data row24 col9\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row24_col10\" class=\"data row24 col10\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row24_col11\" class=\"data row24 col11\" >0.00%</td>\n",
       "                        <td id=\"T_aa162_row24_col12\" class=\"data row24 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f8d03a39e20>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_s.style.format(\"{:.2%}\").background_gradient(cmap='YlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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VFRW69dZbJUk7duzQ448/rpdeeqnZ41VWVqpTp0669dZbdfvtt+vBBx9MzQtRy1pkLMk6b7C8WNKx7t5b0n2Snkp6ILOrzWyhmS18//33v1aiAAAAAMJVWVmpdu3a6Zprromv69Onj373u9/FCxtJmjt3rvLy8nTsscdq5MiR6t+/v3r27KlJkyYlPe6QIUO0cePGlObakkJmg6SjE5ZzFbS6xLn7p+6+Ofb4OUntzKxTwwO5+yR3H+DuA3JycnYjbQAAAACpVlVVpf79+zdaX1hYqL322kvLli2TJFVUVKikpESS9PDDD2vRokVauHChJkyYoE2bNjV6/owZMzRy5MiU5tqSQmaBpK5m1sXM9pFULOmZxB3M7Agzs9jjgbHjNn4FAAAAADJSSUmJKioqtG3bNj399NM6//zzJUkTJkxQ7969NXjwYK1fv16vv/56/DlDhw7VYYcdphdeeEEXXnhhSvPZaSHj7tsk/UDSXyStkjTN3Vea2TVmVtfmdJ6kqtgYmQmSit29YfczAAAAAK1Yz549tWjRoqTbSkpKNG3aNL3wwgsqLCzUYYcdptmzZ+uFF17Qyy+/rGXLlqlv376qra2NP6eyslJvvvmmevbsqfHjU3uHlhbdR8bdn3P3bu6e7+53xNY94O4PxB7f7+493b23uw929/kpzRIAAABA6E477TR98cUX9QblL1iwQHPmzFF+fr46duyo0tLSeLeyTz75RIcccoj2339/rV69Wq+88kqjY2ZnZ+vee+/VI488og8//DBlubaokAEAAACw5zMzPfnkk5o5c6by8/PVs2dPlZWV6aijjpIUtMqsXr1ao0aNkiSdddZZ2rZtmwoLC3XLLbdo8ODBSY975JFHqqSkRBMnTkxZri2ZfhmApLKyMpWVlUWdBgAAaEN2Nl1yGI466ihNmzYt6baxY8dq7Nix8eV9991Xzz//fNJ9q6ur6y3fd999KctRokUGAAAAQAaikAEAAACQcShkAAAAAGQcChkAAAAAGYdCBgAAAEDGoZABAAAAkHGYfhkAAABAXFZWlgoKCuLLxcXFqq2t1RdffKFf/vKX8fVLly5VSUmJVq1alfQ4ZWVlevDBB5WTk6OtW7fqlltuid9IMxUoZICv6YjKpXp3aJ+o0wAAAG3AhtJ5KT1e7p2n7nSf7OxsLV26tN66NWvWaNiwYfUKmYqKCl144YXNHmvs2LEaN26cXn/9dfXv31/nnXee2rVrt0u5N0TXMgAAAADNOv7443XwwQfr1Vdfja+bNm2aiouL9eCDD+rEE09U7969de6552rLli2Nnt+1a1ftv//++uijj1KWE4UMAAAAgLiamhr16dMn/jN16lRJUklJiSoqKiRJr7zyijp27KiuXbtq9OjRWrBggZYtW6bu3bvroYceanTMxYsXq2vXrjrssMNSlieFDLCryjoEPwAAoJGCKQUqmBKMs5h4zSxJqe8mhXDUdS2r+xkzZoykYKzM448/rh07dqiioiI+3qWqqkqnnnqqCgoK9Oijj2rlypXxY/32t7/V8ccfr0GDBqmsrCyleVLIAAAAANipo48+Wnl5eZozZ46eeOIJXXDBBZKkyy67TPfff79WrFihW2+9VbW1tfHnjB07VmvWrNHUqVN1ySWX1Nu2uyhkAAAAALRISUmJxo4dq/z8fOXm5kqSPvvsMx155JH68ssv9eijjyZ93ujRozVgwABNmTIlZblQyAAAAACIazhGprS0NL7t/PPP18qVK1VcXBxfd9ttt2nQoEE6/fTTdcIJJzR53PHjx+uee+7Rjh07UpIn0y8DAAAArVRLpktOte3btze5LScnR19++WW9dd/73vf0ve99r9G+DcfE9O/fX2vWrElJjhItMgAAAAAyEIUMAAAAgIxDIQMAAAAg41DIAAAAAMg4FDIAAAAAMg6FDAAAAICMQyEDAAAAQJI0duxY3XvvvfHlM888U1deeWV8+Uc/+pHuueceZWdnq0+fPurRo4cuueSSRlMypwP3kQEAAEDofjNmhMZ0+UmT2wumFEiSVly6Il0pZYSG92IJ+3gnnXSSHnvsMd14443asWOHPvjgA3366afx7fPnz9e9996r/Px8LV26VNu3b9fpp5+uadOm6aKLLkpprjtDiwwAAAAASdLJJ5+s+fPnS5JWrlypXr166cADD9RHH32kL774QqtWrdIhhxwS3z8rK0sDBw7Uxo0b054rLTIAAAAAJElHHXWU9t57b7311luaP3++hgwZoo0bN+rll19Whw4dVFhYqH322Se+f21trV599VX97ne/S3uutMgAu6muKRwAAGBPUNcqU1fIDBkyJL580kknSZLeeOMN9enTRx07dtQxxxyjwsLCtOdJIQMAAAAg7qSTTtL8+fO1YsUK9erVS4MHD9bLL7+s+fPn6+STT5ak+BiZtWvX6pVXXtEzzzyT9jwpZAAAAADEnXzyyXr22Wd16KGHKisrS4ceeqg+/vhjvfzyyxoyZEi9fY888kjdeeed+uUvf5n2PClkAAAAAMQVFBTogw8+0ODBg+ut69Chgzp16tRo/5EjR2rLli2aN29eOtNksD8AAADQWqV6+uWWyMrKqjflsiRNnjw5/jgvL09VVVXxZTPTsmXL0pVeHIUMUKesQ/Bvl2OizQMAAAA7RdcyIIlVJ3SPOgUAAAA0g0IGAAAAQMahkAEAAACQcShkgGb8ZsyIqFMAAABAEhQyAAAAADIOhQwAAACAODPTxRdfHF/etm2bcnJyNGJE0FNl8uTJysnJUd++fdW1a1edeeaZmj9/fnz/yy67TF26dFHv3r3VrVs3XXLJJdq4cWPK82zR9Mtmdpak30nKkvTf7n5nE/udKOkVSWPc/fGUZQkAAAC0QS/Oyk/p8b592hs73ad9+/aqqqpSTU2NsrOzNXPmTHXu3LnePmPGjNH9998vSaqsrNTo0aNVWVmp7t2DmV9//etf67zzzpO7695779XQoUNVVVWlffbZJ2WvZactMmaWJWmipGGSekgqMbMeTez3K0l/SVl2AAAAANJu2LBhmj59uiSpvLxcJSUlTe47dOhQXX311Zo0aVKjbWamsWPH6ogjjtDzzz+f0hxb0rVsoKS17r7O3bdKqpB0TpL9rpP0hKR/pTA/AAAAAGlWXFysiooK1dbWavny5Ro0aFCz+/fr10+rV6/e5e27oiWFTGdJ6xOWN8TWxZlZZ0mjJD2QutQAAAAARKGwsFDV1dUqLy/X8OHDd7q/u+/W9l3RkkLGkqxrmMm9kn7i7tubPZDZ1Wa20MwWvv/++y1MEQAAAEC6nX322Ro3blyz3crqLFmyJD4+Zle274qWDPbfIOnohOVcSW832GeApAozk6ROkoab2TZ3fypxJ3efJGmSJA0YMCD1ZRkAAABatbKysnr/ovW6/PLL1aFDBxUUFGj27NlN7jdnzhxNmjRJlZWVjba5u+677z698847Ouuss1KaX0sKmQWSuppZF0kbJRVLurBBgl3qHpvZZEnPNixiAAAAAGSO3Nxc3XDDDUm3TZ06VX/729+0ZcsWdenSRU888US9Fpcf//jHuu2227RlyxYNHjxYlZWVKZ2xTGpBIePu28zsBwpmI8uS9LC7rzSza2LbGRcDAACAr+2IyqV6d2ifqNNo1VoyXXKqbd68udG6oqIiFRUVSQruE3PZZZc1+fzJkyeHk1gDLbqPjLs/J+m5BuuSFjDuftnupwUAAAAATWvJYH8AAAAAaFUoZAAAABCtsg5RZ4AMRCEDAAAAIONQyAAAAADIOBQyAAAAADIOhQwAAACAep588kmZmVavXi1Jqq6uVnZ2tvr27avu3btr4MCBmjJlSnz/yZMnKycnR3379lXXrl115plnav78+aHm2KLplwEAAACk3xGVS1N6vJbet6e8vFynnHKKKioqVFZWJknKz8/XkiVLJEnr1q3T6NGjtWPHDv37v/+7JGnMmDG6//77JUmVlZUaPXq0Kisr690oM5VokQEAAAAQt3nzZr300kt66KGHVFFRkXSf4447Tvfcc48mTJiQdPvQoUN19dVXa9KkSaHlSSEDAAAAIO6pp57SWWedpW7duunQQw/V4sWLk+7Xr1+/eNezXdm+uyhkAAAAAMSVl5eruLhYklRcXKzy8vKk+7l7s8fZ2fbdxRgZAAAAAJKkTZs2adasWaqqqpKZafv27TIzff/732+075IlS5od/7Kz7buLFhkAAAAAkqTHH39cl1xyid58801VV1dr/fr16tKlizZs2FBvv+rqao0bN07XXXdd0uPMmTNHkyZN0lVXXRVarrTIAAAAAJAUdCsrLS2tt+7cc8/VL37xC73xxhvq27evamtrdeCBB+q6666Lz1gmSVOnTtXf/vY3bdmyRV26dNETTzwRaosMhQwAAADQSrV0uuRUmT17dqN1119/va6//vpmn3fZZZfpsssuCyepJtC1DAAAAEDGoZABAAAAkHEoZAAAAJB2L87KjzoFZDgKGQAAAAAZh0IGAAAAQMahkAEAAACQcShkAAAAAMS9++67Ki4uVn5+vnr06KHhw4frH//4h7Kzs9W3b191795dAwcO1JQpU3Z6rNmzZ6tDhw7q27evTjjhBI0bNy5leXIfGQAAAKCVyiudntLjVd/5nWa3u7tGjRqlSy+9VBUVFZKkpUuX6r333lN+fr6WLFkiSVq3bp1Gjx6tHTt21LspZjKnnnqqnn32WdXU1Khv374aNWqUTj755N1+LbTIAAAAAJAkVVZWql27drrmmmvi6/r06aOjjz663n7HHXec7rnnHk2YMEGS9Nprr+mkk05S3759ddJJJ2nNmjWNjp2dna0+ffpo48aNKcmVQgYAAACpVdYh6gywi6qqqtS/f/8W7duvXz+tXr1aknTCCSdo7ty5WrJkiX7+85/rpz/9aaP9P/roI73++uv65je/mZJc6VoGAACA0Kw6obtUNDHqNBACd48//uSTT3TppZfq9ddfl5npyy+/jG+bN2+eCgsLtWbNGpWWluqII45ISXxaZAAAAABIknr27KlFixa1aN8lS5aoe/fukqRbbrlFQ4cOVVVVlf785z+rtrY2vt+pp56q5cuXa8WKFfr973+vpUuXpiRXChkAAAAAkqTTTjtNX3zxhR588MH4ugULFujNN9+st191dbXGjRun6667TlLQItO5c2dJ0uTJk5Meu1u3brr55pv1q1/9KiW5UsgAAAAAkCSZmZ588knNnDlT+fn56tmzp8rKynTUUUfpjTfeiE+/fMEFF+i6666Lz1h200036eabb9bJJ5+s7du3N3n8a665RnPnztU///nP3c6VMTIAAABAK7Wz6ZLDcNRRR2natGmN1tfU1DT5nCFDhugf//hHfPm2226TJBUVFamoqCi+Pjs7m1nLAAAAALRdFDIAAAAAMg6FDAAAAICMQyEDAAAAIONQyAAAAADIOBQyAAAAADIOhQwAAACAuOrqavXq1aveurKyMt19992SpLvvvlsnnHCCevXqpd69e+uRRx6RFEy1vHDhwrTlyX1kAAAAgNaqrEOKj/fJbj39gQce0MyZM/Xaa6/poIMO0ieffKKnnnoqNbl9TRQyAAAAAFrkF7/4hSorK3XQQQdJkjp06KBLL700klzoWgYAAABgp2pqavTZZ58pPz8/6lQktbCQMbOzzGyNma01s9Ik288xs+VmttTMFprZKalPFQAAAEDYzCzp+h07djS5LQo7LWTMLEvSREnDJPWQVGJmPRrs9qKk3u7eR9Llkv47xXkCAAAASIOOHTvqo48+qrfuww8/VF5entq3b69169ZFlFl9LWmRGShprbuvc/etkioknZO4g7tvdnePLbaX5AIAAACQcQ444AAdeeSRevHFFyUFRcyMGTN0yimn6Oabb9a1116rTz/9VJL06aefatKkSZHk2ZLB/p0lrU9Y3iBpUMOdzGyUpF9KOkzSd1KSHQAAAIC0e+SRR3TttdfqRz/6kSTp1ltvVX5+vr73ve9p8+bNOvHEE9WuXTu1a9cuvk+6taSQSdYRrlGLi7s/KelJM/umpNsk/VujA5ldLelqSTrmmGO+XqYAAABAW7Ob0yXvqh49eqiysrLRejPTTTfdpJtuuqnRttmzZ6chs6+0pGvZBklHJyznSnq7qZ3dfa6kfDPrlGTbJHcf4O4DcnJyvnayAAAAACC1rJBZIKmrmXUxs30kFUt6JnEHM/uGxaYwMLN+kvaRtCnVyQIAAACA1IKuZe6+zcx+IOkvkrIkPezuK83smtj2BySdK+kSM/tSUo2kMQmD/wEAAAAgpVoyRkbu/pyk5xqseyDh8a8k/Sq1qQEAAABAci26ISYAAAAAtCYUMgAAAAAyTou6lgEAAABoG7KyslRQUBBfLi4uVmlpqYqKivTOO+9o33331datW/Vv//Zvuv3223XwwQc3e7y8vDwdeOCBMjMdcsgheuSRR3Tsscfudp4UMgAAAEArVTClYOc7fQ0rLl2x032ys7O1dOnSpNseffRRDRgwQFu3btXNN9+sc845R3PmzNnpMSsrK9WpUyfdeuutuv322/Xggw9+3dQboWsZACBt8kqnR50CACAF9tlnH91111166623tGzZMknSyJEj1b9/f/Xs2VOTJk1K+rwhQ4Zo48aNKcmBQgYAAABAXE1Njfr06RP/mTp1atL9srKy1Lt3b61evVqS9PDDD2vRokVauHChJkyYoE2bGt9WcsaMGRo5cmRK8qRrGQAAAIC45rqWNZR468gJEyboySeflCStX79er7/+ujp27ChJGjp0qN577z0ddthhuv3221OSJy0yAAAAAL627du3a8WKFerevbtmz56tF154QS+//LKWLVumvn37qra2Nr5vZWWl3nzzTfXs2VPjx49PSXwKGQAAAABfy5dffqmbb75ZRx99tAoLC/XJJ5/okEMO0f7776/Vq1frlVdeafSc7Oxs3XvvvXrkkUf04Ycf7nYOFDIAAAAA4hqOkSktLY1vu+iii1RYWKhevXrp888/19NPPy1JOuuss7Rt2zYVFhbqlltu0eDBg5Me+8gjj1RJSYkmTpy423kyRgYAAABopVoyXXKqbd++Pen62bNnN/mcfffdV88//3zSbdXV1fWW77vvvl1NrR5aZAAAAABkHAoZAAAAABmHQgYAAABAxqGQAQAAAFqRxHuztBW78popZAAAAIBWYr/99tOmTZvaVDHj7tq0aZP222+/r/U8Zi0DAAAAWonc3Fxt2LBB77//ftSppNV+++2n3Nzcr/UcChkAAACglWjXrp26dOkSdRoZga5lAAAAADIOhQwAAACAjEMhAwAAACDjUMgAAAAAyDgUMgAAAAAyDoUMACAyq07oHnUKAIAMRSED7IK80ulRpwAAANCmUcgAAAAAyDgUMgCA9CrrEHUGAIA9AIUMAAAAgIxDIQMAAAAg41DIAAAAAMg4FDIAAAAAMg6FDAAAAICMQyEDAAAAIONQyAAAAADIOBQyAAAAADIOhQwAAACAjEMhAwAAACDjUMgAAAAAyDgUMgAAAAAyTosKGTM7y8zWmNlaMytNsv0iM1se+5lvZr1TnyoAAAAABHZayJhZlqSJkoZJ6iGpxMx6NNjtn5K+5e6Fkm6TNCnViQIAAABAnZa0yAyUtNbd17n7VkkVks5J3MHd57v7R7HFVyTlpjZNAAAAAPhKSwqZzpLWJyxviK1ryhWSnt+dpAAAAACgOXu3YB9Lss6T7mg2VEEhc0oT26+WdLUkHXPMMS1MEQAAAADqa0mLzAZJRycs50p6u+FOZlYo6b8lnePum5IdyN0nufsAdx+Qk5OzK/kCAAAAQIsKmQWSuppZFzPbR1KxpGcSdzCzYyT9SdLF7v6P1KcJAAAAAF/Zadcyd99mZj+Q9BdJWZIedveVZnZNbPsDksZL6ijpv8xMkra5+4Dw0gYAAADQlrVkjIzc/TlJzzVY90DC4yslXZna1AAAAAAguRbdEBMAAAAAWhMKGQAAAAAZh0IGAAAAQMahkAEAAACQcShkAAAAAGQcChkAAAAAGYdCBgAAAEDGoZABJOWVTo86BQAAAHwNFDIAAAAAMg6FDAAAAICMQyEDAAAAIONQyAAAAADIOBQyAAAAADIOhQwAAACAjEMhAwAAACDjUMgAAAAAyDgUMgAAAAAyDoUMAAAAgIxDIQMAAAAg41DIAAAAAMg4FDIAgNatrEPUGeyxfjNmRNQpAMAuo5ABAAAAkHEoZAAAABCZvNLpUaeADEUhAwAAACDjUMgAALCH44o3gD0RhQwAoFU6onJp1CkAAFoxChkAAAAAGYdCBgAAAEDGoZABAAAAkHEoZAAAAABkHAoZAAAAABmHQgYAAABAxqGQAQAAAJBxKGQAAEBSBVMKok4BAJpEIQMAAAAg41DIAAAAAMg4FDIAAAAAQlNWVqaysrKUH5dCBgAAAEDGoZABAAAAkHFaVMiY2VlmtsbM1ppZaZLtJ5jZy2b2hZmNS32aAAAAAPCVnRYyZpYlaaKkYZJ6SCoxsx4NdvtQ0vWS7k55hgAAAACalVc6PeoU0q4lLTIDJa1193XuvlVShaRzEndw93+5+wJJX4aQIwAAAADU05JCprOk9QnLG2LrAAAAACASLSlkLMk635VgZna1mS00s4Xvv//+rhwCAAAAAFpUyGyQdHTCcq6kt3clmLtPcvcB7j4gJydnVw4BAAAAAC0qZBZI6mpmXcxsH0nFkp4JNy0A2HPllU5vk4MyAQBIpb13toO7bzOzH0j6i6QsSQ+7+0ozuya2/QEzO0LSQkkHSdphZjdK6uHun4aXOgAAAIC2aqeFjCS5+3OSnmuw7oGEx+8q6HIGAAAAAKFr0Q0xAQAAAKA1oZABAAAAkHEoZAAAAABkHAoZAAAAABmHQgYAAABAxqGQAQAAAJBxKGQAAAAAZBwKGQAAAAAZh0IGAAAAQMahkAEAAACQcShkAAAAAGQcCpkk8kqnR50CAAAAgGZQyAAAAADIOBQyAAAAADIOhQwAAACAjEMhAwAAACDjUMgAAAAAyDgUMgAAAAAyDoUMAAAAgIxDIQMAAAAg41DIAAAAAMg4FDIAAAAAMg6FDAAAAFImr3R61CmgjaCQAdo4vnAAAEAmopABAAAAkHEoZAAAAABkHAoZAAAAABmHQgYAAABAxqGQAQAAAJBxKGQAAAAAZBwKGQAAAAAZh0IGAAAAQMahkAEAAACQcShkAAAAAOyyvNLpkcSlkAEAAACQcShkAAAAAGQcCplWKKrmOQAAUo3vNABhoZABAAAAkHEoZAAAAABknBYVMmZ2lpmtMbO1ZlaaZLuZ2YTY9uVm1i/1qQIA2qqCKQUqmFIQdRoAgFZkp4WMmWVJmihpmKQekkrMrEeD3YZJ6hr7uVrS71OcJwAAAADEtaRFZqCkte6+zt23SqqQdE6Dfc6R9IgHXpF0sJkdmeJcAQAAAEBSywqZzpLWJyxviK37uvsAAAAAQEqYuze/g9n5ks509ytjyxdLGuju1yXsM13SL939b7HlFyXd5O6LGhzragVdzyTpeElrdiP3TpI+2I3n7462Gjvq+MRue/GJ3fbiE7vtxSd224tP7LYXf3diH+vuOck27N2CJ2+QdHTCcq6kt3dhH7n7JEmTWhBzp8xsobsPSMWxiJ0Z8Ykdjbb62ttq7KjjEzsabfW1t9XYUccndjT2xNfekq5lCyR1NbMuZraPpGJJzzTY5xlJl8RmLxss6RN3fyfFuQIAAACApBa0yLj7NjP7gaS/SMqS9LC7rzSza2LbH5D0nKThktZK2iLp38NLGQAAAEBb15KuZXL35xQUK4nrHkh47JKuTW1qO5WSLmrEzqj4xG578Ynd9uITu+3FJ3bbi0/sthc/lNg7HewPAAAAAK1NS8bIAAAAIMOYWYt63gCZikIGAABgz/Ra1AkAYaKQAQAA2DNZpMHNRkcZPypmNjnqHNqKjBwjY2b5kkokFbt7r4hyODoW/9chxtjL3Xc0se1gd/84rNixGIPd/ZUwY3wdZrafpO+6+2NR57KnMrPT3H1W7HEXd/9nwrbR7v6n6LILh5n9WVKTH4TufnaIsSc0t93drw8rdkIO7dz9y7DjNBH7TEkHuvvjDdZfJOlf7j4zirzCZmY/cPf7o86jtTGzY939zYhih/o+MLOD3P3TJrYd4+5vhRR3g6R7mtru7k1uS1H8xe7eL8wYO4nfS9JNknoo+Jz/u6TfuPvykONG/bqPd/ekN503s5Pd/aV05xSWjOk7aWZHShoj6UJJhZJ+qaCYSWcOnSSdH4vbWdKTIYdcaGbfc/dXG+RxpaSfSjou5Pi/N7PXJP0k7KKpKWaWJekMBf/nZ0qaJynUQsbMBimYXSNf0gpJV7j738OMmRC7q6T/lPShgi+fByV9U8HU5le6+4KQU7hbUt2H7xMJjyXp/0oKtZCJvc+vVfClI0kLJf0/d98UYti7Qzz2zlwjqUrSNAU3EY7i6ulGM3taUrmkSk/v1a2fSfpukvUvKvh8DbWQieokR9LlkiIrZMzsh81tT8PJ7RAF36Fz3f1fZlYoqVTSqap/c+1QmZlJGqrgvOK7kg4PMdxsxT5PzexFd/92wranVP+zNpWyJB2giFtmomBm5yj4fP9l7F+T1F/SE2Y2zt2fDjH8/mbWV038v7v74hBjS9IqM/uDpGvdfXODbfcpvL83mdln+uriYN3rdwU1xz7untLao9UXMmZ2lYKT2FwFX/ZXSnra3X+WpvgHShql4IOum4Iv1+PcPTcN4a+XNKmumJB0rKT/krRBwclt2PrHcnjNzG5z9z+kIaYkycy+qeD//DsK+vieLKmLu29JQ/iJksZJmivpbEm/VVBEpcP/SHpE0kGSXpV0o4K/v1MVnPgMCjm+NfE42XJqA5t9S9L/SnpY0uRYvH6SZpnZSEk/d/eLQwj9d0k5DYtVM+sp6V8hxEt0pIKLI2MkbZM0VdIT7v5RyHETdZd0nqRbJD1iZo9LKm94ASUk+7v7+w1Xuvu7ZtY+zMARn+RE7cCoApvZryWNkLRU0k/M7FlJ35f0CwUFXjpyGKTg+2WUpEMVXDz5cdhhEx4f2sy2VHvH3X8e4vF35gQzS3ZhwBTcvaMwxNg/l3S6u1cnrFtmZrMkPR37CUtnSb9R8t+tSzotxNiStFLBueJiM7ukQe+aUL/L3b3e50vsPPr7kv5DYTQAuHur/pG0VdIcSQMS1q1LY/yaWPxT9VVXvHTGz5J0u6SPJK2XdEYEv4Mekj6R9JmkT+v+DTHeBknzJV2soNuJJP0zja93cXPLIcdemvB4bVPb0vHa0/3/oKBg7ZtkfZ/Y392UkOJWSPpWkvVnSvpjGn/3nRUU0G9LujhdcRvkcJSkGyS9LOkNSXeEHO8fkvZOsr6dpNdDjr1MUl6S9XmSloUce1vsb7rhT6ifra3hR8GFg/1ijw+Jfcd2TVPsOyS9rqDF70pJHdP13RLVZ6ukJRH/vlcquAib9Cfk2H/flW17yP/74ti/dT06xkvaK3FbGnI4WFKZpHUKzmM7hhGn1bfIKPhiPV/SPWZ2uIJWmXZpjP9TScWSfi/pj2Y2NY2xpa+6sv1e0r9JGmNmC939w3QEN7MrFDT5/6ekiR776wzZE5JGKrhKvT3W7SWd3V0ObjBAsd6yhztOJHFMVMP+1EnHS6XYcWb2jIIrNnWPFVvuEnLsA9x9ScOV7r7UzN6T9O8hxS1w9zlJ4v7FzH4TUsx6zKyfgvf56ZKel7QoHXEbcve3zewhBRdOfqjgZO8/Qwz5J0kPxsaMfC5JsZaYCQq5G6Okdl7/Sq0kyd2rzSzs75gV7t435BhNMrPxzWx2d78txPA17l4bC/SRma1x99dDjJfoaklrFHyfPuvutWaWru+Ww2Jd+izhsWLLOSHG/fbOdwnVVo9o3JOkL5ONPzKzYxVcTNiTmSS5+1wzG6CgN8+82PjDcAMHwzB+pOAc7mEFFyg/CS1ees5LU8PMchUUFSWS9pf0pLv/NE2xj4vFLZbUVdKtsfj/CDHmCwquVl3v7v+M9ef9gaSxku5091Dv0Gpm8yVVS/qhu78bZqwksev6LpdIGq6gq9UVkp7zxv09Ux37f5rZ7O4eWvcHM9ui4OqJKRijs7Zuk4IujWF3t/lWc9uTnfCnMPYqSSd5g25VZnaopJfcvXtIcf/h7t2a2LbG3Y8PI27s+D9T0M1mlYKWoRnunvYv2LqJNBS8306WNCOWz1/dfXuIcfdWcKXuSklvKvg7P1rSQ5Ju8XAHXy9TMHlIspOcP3uIXV7MbEnEhcyPkqxur+AztqO7HxBi7I8VdNut883EZQ93co3EMZenSapUcIHw6LDfd2Z2a3PbPU3d5dPNzO539x9EFHukpLsUdFtcpOCC6IkKLs7+xN2fCjH2xd5Ed3wzO9Xd54UVOxaj0WQDZnapgs/b/d29Y4ixP5f0voKu8p813O4pHoOXEYVM7I/xGwquYv0ltu54BbOGpf3Nb2YFCvrXXuDu+SHGGeXujfoTmtkRku529/8TVuxYnNO9FcwaFLs6epaCL58z3L1TxCmFJnYS1aR0Xtkys5xYzEZjGEKKd7WkqxR0r6obCNlf0q8kPRRW4W5m0xW0Nj7XYP0wBRcRhoURNxZjh4Jm95rYqsQBkh7myXRCDn9UcDI3V0Hx8mzdFfN0MbNsBZ/xUtClsqa5/VMUc6SiO8m5JeRWjxaL9V+/QUERM03BZAehjQ2L8mJJgzz2U3AR4UIFxfuL7n5hOmK3JbH3WWd3nxhbflVftUD9xEOehdTMeitoHeip4HN1pYLzp2Uhx10n6QFJ99QVybFeRb+RdLy7nxhy/B2STnH3+Q3WHyfpJne/JsTYZWp+JtCUnre3+kLGzP5LwR/gfAVNpH+O+gsg1my2KU3drJLFD33q51icyKeGTcjlEEkfK+hbHepJjpld0sxmb+oqSwrjj1SDwj1dYi1h4yVdp+BDfy8FTfD3eRoGjJrZCAWzSPXUV7NI/drd/xxizG6SnlXwGVPXpWuApCGSRoTc6hp54Rq7Svcnd2905SwNsZu9x0TI3TijPMmJXy01s/vc/bow4zWRw6EKug9eJGmKpN81bA1Ncz5p+V5rIvaBkka7+5QQY0xz9wtij3/l7j9J2PZXdz8jrNhRMrOXFPxe18eWlyo4l2sv6X+8/uxtewwzO1jBRbiTFFwoKFDwfrtL0u+9iVtrpDD+jQq6dh2pYBKZcndfGmbMqGRCIVMlqbe7bzez/SXNc/f+aYw/WNKdCqbDvU3SHyR1UnCCd4m7z0hTHo2mfnb3cSHH3KpmpoYN60M/1n97mruvNrN9FXRz6a3ghPpCd38hjLgJ8e9LtlpB15vOnuKpAxvEjrRwN7OxCrryXe2xe8jEruD8XkG3p9+mIYdO7v5B2HEaxNxXwZXZnrFVKxUM9P8iDbFHKqLCNRb/R6p/9cwlfSDpb55wH6GQYu9QMHvV0rpViXmE2Y0zSoldy5J1AUlD/F9LGq1gmvmJYXfXbSaPtH6vmdmJktbXdZWOXbQ6V0G3xjIPcexpc7/zqLsahsnMFiS2PiR2NTOzV9x9cIixn2lue5jdGBNyuEHBzKdvSxrs7hvCjtkg/rEKhkQUS9pPwTT75R7yuLRYj4abVX9q+1817PmQklgZUMg0fMOn9UPfzBYqGPDfQcGH/jB3f8XMTlDwxxDah48ln/p5jKdn6meZWUdFMDWsma2U1MvdPdbdqERB15duCmauGhhm/Aa5mIIrlj9R8Ea8w0O8x0QrKNyXKJiu8oMG63MUjJcI8+/9uwoGBn6pYGKDCxo2i4cUt27O+4ZTUrqkLxTM3vWf7v5iCLEjb3Fuou/+oQpmbStz94oQY49S8PnyDQVToZa7+9rmn5Wy2JGd5DRokYmikNmh4G97m+oXsXVdGg8KMXZk32tmtljSv7n7hxZM8V+hoPW5j6Tu7n5emLGb+p1H8TeQLma21t2/0cS2Nzzc7vnvK5jttVzB7QwaXowNc8znwQpaZAYp6GUwXMFn/A0eu+l0ullwX5uHJRW6e1aIca5SMNXyTQruBScFvRzulPTfKe8m7hFOD9eSH0lbJC2P/axIWF6hkKfIjMVfmvB4VYNtS0KOHenUzw1ySdvUsIn/rwpmMPuPhOV0TRu4t4IByKsU3NPk+DTFjWzq51i8ql3ZlqLYyyWdEHs8SNKcdL72JnLKUtAaGMprV9DimRV7vL+kRVG/5oTcDk3j+629ghPbpyX9TUmmww4h5vsKxmL9WMGA828l/oQcO/F7rOF33PKof/chv/bIvtcSzxkU3C+sLGF5acixV0vqq2Dc3yoF98jqV7cc9e8lxNf9qKSrkqz/DwUXLsKMnaVgfO0USUsUDHTvmabX/UbsnGnvhHV9FFy0CvV1N8ijnYLeJI9KelfBBemRIcf8u6RDk6zvGMbfeiZMv5xspiJTcIPMdMxYltiPseHYjLCbs6Ke+lmSZOmfGvYLC+64/Z6koQo+DOrsH3Jsmdm1Cvq0vijpLE/v1JGJNw8zSfkJy/LwB39v3cVtqbDN3VdLkru/GrtyGykPZuxa1kR3w1TYGoshd98SawFsFTy4ap2ufGoV3KvqU0nHKOgCEbYjFHymlSgooqYrOMFYmYbYoczAlyGi/F7LMrO9PRh8/W0F0zHXCft86B19dYPEdxXchLVOWmcFTbOxkp4yswtVfxKXfRXcZiE0sc/WGZJmxLoPl0iabWY/d/ewPtMTwvvdDVYslXRSrMUiVGZW99lWd1PxCgVdxj8PO7aCCxSNumm6+6YwvlJafdeyRGbWR7HZwiT9U0E3p/tDjrld0ucKPnyyFVw9U2x5P3cP/Z42FsHUz7G4kUwNGxuXNFnBzCb3eqyrjZkNV9AaVBJy/B0K7uj+vpIUq2EWE2bWVdLhCprDEx0r6W0PudtNwt97o00K+e/dzDZISpyW8YeJy57iKRtbAzOrUXCDvrpP97opt9M2a1kzuZ0m6f+6e2h3oDazuinWB0p6QVKFuy9s/lmh5FF3kvNrSek4yWkYv6OCVqG33D2SewilWxTfa2b2nwq6+HygoGDu5+5uZt9Q0G355BBjD1QwPued2PKlCsbnVCvk8TlRqus2Z2bfVjBeQpJWepq6V8Xe299R8LeWJ+kZSQ+7+8aQ40baXdDMKiX9UcF5clr/tiyYme5qbzBpSmxylQc9xcMDWn0hY8GMQnX3jtmkoFlsnLs3O9vPnsrSNPVzLFbkU8NGIcpiwsyelfRTbzAOx4IbWt3q7t8NK3bUmhirEed74H0WYgMxm5um8q2mtqUwhxVJcjhUQTfSS+payUKKvUNBl6q/xXKol4eHPDNihCc5z0oqdfcqMztSwZXqhQoK2Unufm+Y8Vub2PdaiYKxMmF/rw1WMJPTX/2rm7B2U3BD3sXNPnn34kY2PidKUU5kYGZTJPVS0JOkwt2r0hi74YW5evbEC3N1zOwUBV3Z/kf1p7a/VNL/cfe/pTReBhQyOyTNk3RF3Qmkma1z9+OizSwalsapny2iqWEtuOPxJ+7+UIP11ykYT3BvGHET4kRWTJhZlbv3amLbCncvCCs20i9hooFkQp1oICGHhu9zV/AZ87mZ3Rjm+83MLlPzhVyY0+FGeZKz0t17xh7/VMHYsEti3Slf2lMvEklSrPXjcHd/qcH6byr8C0X7SbpGsVkCFdyfKi03oDWzZe7eO/Z4oqT33b0strzU3fukI490i/KEPnb+WNfDIN2TWryjoPtk0r5Ue+KFuUQW3O/w+6o/tf1ED+Hm6pkwRuZcBS0ylWZWd7fpVtOPPEzWzNTPZhb61M/JCpU0FVKXKxgE2dAkSQsk3RtibEnKa1jESJK7LzSzvJBjNzc2IDvk2JGyVnTfonRx9ybHAVlwF/JeCq5sJS1uU5RDcxckfqgQ32/uPjmsY7fAxQpOcrpJuj6h73boJzkKZuar821JDyoI+lns5GtPdq+Sj2/domCa2jBbnaco+L+fJ2mYgq5ON4QYL1GU43OilCXpAEVz3rYsqtYgSe94Gu691lrFCpbx6YjV6t88HtzZ/kkza69gYNhYSYeb2e8V9Kf9a5T5hex+fTX18yw1mPpZwSC20ERYSLm7NxpY7u5fpGnwcZTFxAIzu8rdH0xcaWZXKPxJFqKW+Pp+pqDPfJvl4U800BKhvt8swimQ3X2vsI7dAutjLcwbFFy0mSFJZpatYJahPVmUF4p61LVqm9lDCgZBp0u5pDlm9oGC7trzYnl8Q8FEF3uqKE/oo+xy1CYuuCfTRHdlKaRhCa2+kKkT68v6qKRHLbgj8fmSSiXtyYXM3nWFWmyWjVckyYMbRaYjfmSFlJkd7u7vNVwXVrwGoiwmblRQuF+k+neZ30fBvRf2WIndiGJdmkLrVpRJ3P3/RRk+5OMPUTP3ediDXSHp5wrujzXG3T+OrR+soF/5nizKC0XxljB335bOSQLd/Q4ze1Ffjc+pe2/tpWCszJ4qyvf0YbGu6kmFPE7l2yEeu7WbqOD8MNn3x5hUB2v1Y2TaMov4BlqJ/XbNbJW7d0/YFtoAPgvutny9pB+p/nSNdynoYxnqCW6sYHpSwXTDjYqJMPp4JslhqL7qTpS2GV5ai6hnfGlLmhmnY5Ky3T20C16x7nN104QWKr1TICMCZlYuaVYTF4rOcPeUn+gkxEiclTFxJtJ0dCdsk8zsUI9oRra2Pk4lKrH32RwFs8xubLAt5d/tFDKtmEU89XOUhZSZDVPQ4lZ3Ml8l6U53fz6smElyaNPFRJQoZNoei3gK5HSKsktd1FrDhSK0DXyPRMPMlkj6LwVjZH7o7o8lbkv1RXAKGTQp6kIKbUuDloH9Vf/vjaule6iopkCOkpm9r2a61Ln7nCjySicuFCFsUU793JbZV/cO6qZgSEiVpGs9uOlzyovLjBkjg/Rz96wo4rbF2avQ/Axe2DM1mAL5Z+mcAjliR+irLnUXqg11qYtyCmS0OW15nErk3P0fZjZE0u2SlsSGDaQcLTJodcxsq4IKfpqCm/I1vFrJIHBgDxDlfR5ai7bUpU6SzGyq6k+BXO3uN0aaFICUSdYSZmZFkh6WlJPqi5a0yKA1OlLBrHRjJG2TNFXSE+7+UaRZAUipiKdAjlSSLnUTJP0pypzSJMopkAGEr9EkCu4+28z6S/qPVAejRQatmpl1VvBF/0NJP3H3P0ScEgDslgZd6iraUJe6SGbgBLDnopBBq2Vm/RQUMacrmN3mN+7+92izAoDd05a71DEFMoBUopBBq2NmP5M0QtIqSRWSZjAYFAAAAIkoZNDqxK5WrpNUE1tV74/U3QvTnhQAAABaFQb7ozU6XtLhCu6zkOhYBbOYAQAAoI1rszPGoFX7raRP3f3NxB8F/ah/G3FuAAAAaAUoZNAa5bn78oYr3X2hgmlKAQAA0MZRyKA12q+ZbdlpywIAAACtFoUMWqMFZnZVw5VmdoWCaZgBAADQxjFrGVodMztc0pOStuqrwmWApH0kjXL3d6PKDQAAAK0DhQxaLTMbquDu15K00t1nRZkPAAAAWg8KGQAAAAAZhzEyAAAAADIOhQwAAACAjEMhAwAAACDjUMgAAAAAyDgUMgAAAAAyzv8Hvp9vPHXqDoIAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting a comparison of assets weights for each portfolio\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_figwidth(14)\n",
    "fig.set_figheight(6)\n",
    "ax = fig.subplots(nrows=1, ncols=1)\n",
    "\n",
    "w_s.plot.bar(ax=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "w_s = pd.DataFrame([])\n",
    "\n",
    "# When we use hist = True the risk measures all calculated\n",
    "# using historical returns, while when hist = False the\n",
    "# risk measures are calculated using the expected returns \n",
    "#  based on risk factor model: R = a + B * F\n",
    "\n",
    "hist = True\n",
    "for i in rms:\n",
    "    w = port.optimization(model=model, rm=i, obj=obj, rf=rf, l=l, hist=hist)\n",
    "    w_s = pd.concat([w_s, w], axis=1)\n",
    "    \n",
    "w_s.columns = rms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        }</style><table id=\"T_fe3f3_\" ><thead>    <tr>        <th class=\"blank level0\" ></th>        <th class=\"col_heading level0 col0\" >MV</th>        <th class=\"col_heading level0 col1\" >MAD</th>        <th class=\"col_heading level0 col2\" >MSV</th>        <th class=\"col_heading level0 col3\" >FLPM</th>        <th class=\"col_heading level0 col4\" >SLPM</th>        <th class=\"col_heading level0 col5\" >CVaR</th>        <th class=\"col_heading level0 col6\" >EVaR</th>        <th class=\"col_heading level0 col7\" >WR</th>        <th class=\"col_heading level0 col8\" >MDD</th>        <th class=\"col_heading level0 col9\" >ADD</th>        <th class=\"col_heading level0 col10\" >CDaR</th>        <th class=\"col_heading level0 col11\" >UCI</th>        <th class=\"col_heading level0 col12\" >EDaR</th>    </tr></thead><tbody>\n",
       "                <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row0\" class=\"row_heading level0 row0\" >APA</th>\n",
       "                        <td id=\"T_fe3f3_row0_col0\" class=\"data row0 col0\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row0_col1\" class=\"data row0 col1\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row0_col2\" class=\"data row0 col2\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row0_col3\" class=\"data row0 col3\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row0_col4\" class=\"data row0 col4\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row0_col5\" class=\"data row0 col5\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row0_col6\" class=\"data row0 col6\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row0_col7\" class=\"data row0 col7\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row0_col8\" class=\"data row0 col8\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row0_col9\" class=\"data row0 col9\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row0_col10\" class=\"data row0 col10\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row0_col11\" class=\"data row0 col11\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row0_col12\" class=\"data row0 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row1\" class=\"row_heading level0 row1\" >BA</th>\n",
       "                        <td id=\"T_fe3f3_row1_col0\" class=\"data row1 col0\" >7.46%</td>\n",
       "                        <td id=\"T_fe3f3_row1_col1\" class=\"data row1 col1\" >6.61%</td>\n",
       "                        <td id=\"T_fe3f3_row1_col2\" class=\"data row1 col2\" >8.25%</td>\n",
       "                        <td id=\"T_fe3f3_row1_col3\" class=\"data row1 col3\" >6.36%</td>\n",
       "                        <td id=\"T_fe3f3_row1_col4\" class=\"data row1 col4\" >8.43%</td>\n",
       "                        <td id=\"T_fe3f3_row1_col5\" class=\"data row1 col5\" >8.52%</td>\n",
       "                        <td id=\"T_fe3f3_row1_col6\" class=\"data row1 col6\" >8.43%</td>\n",
       "                        <td id=\"T_fe3f3_row1_col7\" class=\"data row1 col7\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row1_col8\" class=\"data row1 col8\" >7.16%</td>\n",
       "                        <td id=\"T_fe3f3_row1_col9\" class=\"data row1 col9\" >13.63%</td>\n",
       "                        <td id=\"T_fe3f3_row1_col10\" class=\"data row1 col10\" >16.38%</td>\n",
       "                        <td id=\"T_fe3f3_row1_col11\" class=\"data row1 col11\" >14.84%</td>\n",
       "                        <td id=\"T_fe3f3_row1_col12\" class=\"data row1 col12\" >6.78%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row2\" class=\"row_heading level0 row2\" >BAX</th>\n",
       "                        <td id=\"T_fe3f3_row2_col0\" class=\"data row2 col0\" >1.03%</td>\n",
       "                        <td id=\"T_fe3f3_row2_col1\" class=\"data row2 col1\" >0.98%</td>\n",
       "                        <td id=\"T_fe3f3_row2_col2\" class=\"data row2 col2\" >0.36%</td>\n",
       "                        <td id=\"T_fe3f3_row2_col3\" class=\"data row2 col3\" >0.96%</td>\n",
       "                        <td id=\"T_fe3f3_row2_col4\" class=\"data row2 col4\" >0.22%</td>\n",
       "                        <td id=\"T_fe3f3_row2_col5\" class=\"data row2 col5\" >3.48%</td>\n",
       "                        <td id=\"T_fe3f3_row2_col6\" class=\"data row2 col6\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row2_col7\" class=\"data row2 col7\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row2_col8\" class=\"data row2 col8\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row2_col9\" class=\"data row2 col9\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row2_col10\" class=\"data row2 col10\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row2_col11\" class=\"data row2 col11\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row2_col12\" class=\"data row2 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row3\" class=\"row_heading level0 row3\" >BMY</th>\n",
       "                        <td id=\"T_fe3f3_row3_col0\" class=\"data row3 col0\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row3_col1\" class=\"data row3 col1\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row3_col2\" class=\"data row3 col2\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row3_col3\" class=\"data row3 col3\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row3_col4\" class=\"data row3 col4\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row3_col5\" class=\"data row3 col5\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row3_col6\" class=\"data row3 col6\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row3_col7\" class=\"data row3 col7\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row3_col8\" class=\"data row3 col8\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row3_col9\" class=\"data row3 col9\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row3_col10\" class=\"data row3 col10\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row3_col11\" class=\"data row3 col11\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row3_col12\" class=\"data row3 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row4\" class=\"row_heading level0 row4\" >CMCSA</th>\n",
       "                        <td id=\"T_fe3f3_row4_col0\" class=\"data row4 col0\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row4_col1\" class=\"data row4 col1\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row4_col2\" class=\"data row4 col2\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row4_col3\" class=\"data row4 col3\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row4_col4\" class=\"data row4 col4\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row4_col5\" class=\"data row4 col5\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row4_col6\" class=\"data row4 col6\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row4_col7\" class=\"data row4 col7\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row4_col8\" class=\"data row4 col8\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row4_col9\" class=\"data row4 col9\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row4_col10\" class=\"data row4 col10\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row4_col11\" class=\"data row4 col11\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row4_col12\" class=\"data row4 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row5\" class=\"row_heading level0 row5\" >CNP</th>\n",
       "                        <td id=\"T_fe3f3_row5_col0\" class=\"data row5 col0\" >17.82%</td>\n",
       "                        <td id=\"T_fe3f3_row5_col1\" class=\"data row5 col1\" >13.21%</td>\n",
       "                        <td id=\"T_fe3f3_row5_col2\" class=\"data row5 col2\" >17.50%</td>\n",
       "                        <td id=\"T_fe3f3_row5_col3\" class=\"data row5 col3\" >12.81%</td>\n",
       "                        <td id=\"T_fe3f3_row5_col4\" class=\"data row5 col4\" >17.99%</td>\n",
       "                        <td id=\"T_fe3f3_row5_col5\" class=\"data row5 col5\" >21.30%</td>\n",
       "                        <td id=\"T_fe3f3_row5_col6\" class=\"data row5 col6\" >31.74%</td>\n",
       "                        <td id=\"T_fe3f3_row5_col7\" class=\"data row5 col7\" >29.80%</td>\n",
       "                        <td id=\"T_fe3f3_row5_col8\" class=\"data row5 col8\" >55.32%</td>\n",
       "                        <td id=\"T_fe3f3_row5_col9\" class=\"data row5 col9\" >12.02%</td>\n",
       "                        <td id=\"T_fe3f3_row5_col10\" class=\"data row5 col10\" >27.50%</td>\n",
       "                        <td id=\"T_fe3f3_row5_col11\" class=\"data row5 col11\" >16.88%</td>\n",
       "                        <td id=\"T_fe3f3_row5_col12\" class=\"data row5 col12\" >45.28%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row6\" class=\"row_heading level0 row6\" >CPB</th>\n",
       "                        <td id=\"T_fe3f3_row6_col0\" class=\"data row6 col0\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row6_col1\" class=\"data row6 col1\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row6_col2\" class=\"data row6 col2\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row6_col3\" class=\"data row6 col3\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row6_col4\" class=\"data row6 col4\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row6_col5\" class=\"data row6 col5\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row6_col6\" class=\"data row6 col6\" >0.41%</td>\n",
       "                        <td id=\"T_fe3f3_row6_col7\" class=\"data row6 col7\" >7.20%</td>\n",
       "                        <td id=\"T_fe3f3_row6_col8\" class=\"data row6 col8\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row6_col9\" class=\"data row6 col9\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row6_col10\" class=\"data row6 col10\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row6_col11\" class=\"data row6 col11\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row6_col12\" class=\"data row6 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row7\" class=\"row_heading level0 row7\" >DE</th>\n",
       "                        <td id=\"T_fe3f3_row7_col0\" class=\"data row7 col0\" >3.77%</td>\n",
       "                        <td id=\"T_fe3f3_row7_col1\" class=\"data row7 col1\" >2.12%</td>\n",
       "                        <td id=\"T_fe3f3_row7_col2\" class=\"data row7 col2\" >2.68%</td>\n",
       "                        <td id=\"T_fe3f3_row7_col3\" class=\"data row7 col3\" >2.91%</td>\n",
       "                        <td id=\"T_fe3f3_row7_col4\" class=\"data row7 col4\" >2.82%</td>\n",
       "                        <td id=\"T_fe3f3_row7_col5\" class=\"data row7 col5\" >3.09%</td>\n",
       "                        <td id=\"T_fe3f3_row7_col6\" class=\"data row7 col6\" >1.63%</td>\n",
       "                        <td id=\"T_fe3f3_row7_col7\" class=\"data row7 col7\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row7_col8\" class=\"data row7 col8\" >5.45%</td>\n",
       "                        <td id=\"T_fe3f3_row7_col9\" class=\"data row7 col9\" >0.38%</td>\n",
       "                        <td id=\"T_fe3f3_row7_col10\" class=\"data row7 col10\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row7_col11\" class=\"data row7 col11\" >1.24%</td>\n",
       "                        <td id=\"T_fe3f3_row7_col12\" class=\"data row7 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row8\" class=\"row_heading level0 row8\" >HPQ</th>\n",
       "                        <td id=\"T_fe3f3_row8_col0\" class=\"data row8 col0\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row8_col1\" class=\"data row8 col1\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row8_col2\" class=\"data row8 col2\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row8_col3\" class=\"data row8 col3\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row8_col4\" class=\"data row8 col4\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row8_col5\" class=\"data row8 col5\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row8_col6\" class=\"data row8 col6\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row8_col7\" class=\"data row8 col7\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row8_col8\" class=\"data row8 col8\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row8_col9\" class=\"data row8 col9\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row8_col10\" class=\"data row8 col10\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row8_col11\" class=\"data row8 col11\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row8_col12\" class=\"data row8 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row9\" class=\"row_heading level0 row9\" >JCI</th>\n",
       "                        <td id=\"T_fe3f3_row9_col0\" class=\"data row9 col0\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row9_col1\" class=\"data row9 col1\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row9_col2\" class=\"data row9 col2\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row9_col3\" class=\"data row9 col3\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row9_col4\" class=\"data row9 col4\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row9_col5\" class=\"data row9 col5\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row9_col6\" class=\"data row9 col6\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row9_col7\" class=\"data row9 col7\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row9_col8\" class=\"data row9 col8\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row9_col9\" class=\"data row9 col9\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row9_col10\" class=\"data row9 col10\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row9_col11\" class=\"data row9 col11\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row9_col12\" class=\"data row9 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row10\" class=\"row_heading level0 row10\" >JPM</th>\n",
       "                        <td id=\"T_fe3f3_row10_col0\" class=\"data row10 col0\" >7.34%</td>\n",
       "                        <td id=\"T_fe3f3_row10_col1\" class=\"data row10 col1\" >11.36%</td>\n",
       "                        <td id=\"T_fe3f3_row10_col2\" class=\"data row10 col2\" >6.11%</td>\n",
       "                        <td id=\"T_fe3f3_row10_col3\" class=\"data row10 col3\" >10.48%</td>\n",
       "                        <td id=\"T_fe3f3_row10_col4\" class=\"data row10 col4\" >5.68%</td>\n",
       "                        <td id=\"T_fe3f3_row10_col5\" class=\"data row10 col5\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row10_col6\" class=\"data row10 col6\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row10_col7\" class=\"data row10 col7\" >7.70%</td>\n",
       "                        <td id=\"T_fe3f3_row10_col8\" class=\"data row10 col8\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row10_col9\" class=\"data row10 col9\" >1.62%</td>\n",
       "                        <td id=\"T_fe3f3_row10_col10\" class=\"data row10 col10\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row10_col11\" class=\"data row10 col11\" >3.93%</td>\n",
       "                        <td id=\"T_fe3f3_row10_col12\" class=\"data row10 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row11\" class=\"row_heading level0 row11\" >LUV</th>\n",
       "                        <td id=\"T_fe3f3_row11_col0\" class=\"data row11 col0\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row11_col1\" class=\"data row11 col1\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row11_col2\" class=\"data row11 col2\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row11_col3\" class=\"data row11 col3\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row11_col4\" class=\"data row11 col4\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row11_col5\" class=\"data row11 col5\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row11_col6\" class=\"data row11 col6\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row11_col7\" class=\"data row11 col7\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row11_col8\" class=\"data row11 col8\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row11_col9\" class=\"data row11 col9\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row11_col10\" class=\"data row11 col10\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row11_col11\" class=\"data row11 col11\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row11_col12\" class=\"data row11 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row12\" class=\"row_heading level0 row12\" >MMC</th>\n",
       "                        <td id=\"T_fe3f3_row12_col0\" class=\"data row12 col0\" >32.90%</td>\n",
       "                        <td id=\"T_fe3f3_row12_col1\" class=\"data row12 col1\" >30.51%</td>\n",
       "                        <td id=\"T_fe3f3_row12_col2\" class=\"data row12 col2\" >35.58%</td>\n",
       "                        <td id=\"T_fe3f3_row12_col3\" class=\"data row12 col3\" >30.30%</td>\n",
       "                        <td id=\"T_fe3f3_row12_col4\" class=\"data row12 col4\" >35.91%</td>\n",
       "                        <td id=\"T_fe3f3_row12_col5\" class=\"data row12 col5\" >36.88%</td>\n",
       "                        <td id=\"T_fe3f3_row12_col6\" class=\"data row12 col6\" >44.73%</td>\n",
       "                        <td id=\"T_fe3f3_row12_col7\" class=\"data row12 col7\" >33.52%</td>\n",
       "                        <td id=\"T_fe3f3_row12_col8\" class=\"data row12 col8\" >15.77%</td>\n",
       "                        <td id=\"T_fe3f3_row12_col9\" class=\"data row12 col9\" >36.47%</td>\n",
       "                        <td id=\"T_fe3f3_row12_col10\" class=\"data row12 col10\" >34.25%</td>\n",
       "                        <td id=\"T_fe3f3_row12_col11\" class=\"data row12 col11\" >36.14%</td>\n",
       "                        <td id=\"T_fe3f3_row12_col12\" class=\"data row12 col12\" >26.19%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row13\" class=\"row_heading level0 row13\" >MO</th>\n",
       "                        <td id=\"T_fe3f3_row13_col0\" class=\"data row13 col0\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row13_col1\" class=\"data row13 col1\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row13_col2\" class=\"data row13 col2\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row13_col3\" class=\"data row13 col3\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row13_col4\" class=\"data row13 col4\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row13_col5\" class=\"data row13 col5\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row13_col6\" class=\"data row13 col6\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row13_col7\" class=\"data row13 col7\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row13_col8\" class=\"data row13 col8\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row13_col9\" class=\"data row13 col9\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row13_col10\" class=\"data row13 col10\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row13_col11\" class=\"data row13 col11\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row13_col12\" class=\"data row13 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row14\" class=\"row_heading level0 row14\" >MSFT</th>\n",
       "                        <td id=\"T_fe3f3_row14_col0\" class=\"data row14 col0\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row14_col1\" class=\"data row14 col1\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row14_col2\" class=\"data row14 col2\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row14_col3\" class=\"data row14 col3\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row14_col4\" class=\"data row14 col4\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row14_col5\" class=\"data row14 col5\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row14_col6\" class=\"data row14 col6\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row14_col7\" class=\"data row14 col7\" >0.96%</td>\n",
       "                        <td id=\"T_fe3f3_row14_col8\" class=\"data row14 col8\" >8.42%</td>\n",
       "                        <td id=\"T_fe3f3_row14_col9\" class=\"data row14 col9\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row14_col10\" class=\"data row14 col10\" >3.35%</td>\n",
       "                        <td id=\"T_fe3f3_row14_col11\" class=\"data row14 col11\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row14_col12\" class=\"data row14 col12\" >7.12%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row15\" class=\"row_heading level0 row15\" >NI</th>\n",
       "                        <td id=\"T_fe3f3_row15_col0\" class=\"data row15 col0\" >11.40%</td>\n",
       "                        <td id=\"T_fe3f3_row15_col1\" class=\"data row15 col1\" >14.79%</td>\n",
       "                        <td id=\"T_fe3f3_row15_col2\" class=\"data row15 col2\" >11.04%</td>\n",
       "                        <td id=\"T_fe3f3_row15_col3\" class=\"data row15 col3\" >15.89%</td>\n",
       "                        <td id=\"T_fe3f3_row15_col4\" class=\"data row15 col4\" >10.64%</td>\n",
       "                        <td id=\"T_fe3f3_row15_col5\" class=\"data row15 col5\" >6.20%</td>\n",
       "                        <td id=\"T_fe3f3_row15_col6\" class=\"data row15 col6\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row15_col7\" class=\"data row15 col7\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row15_col8\" class=\"data row15 col8\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row15_col9\" class=\"data row15 col9\" >20.22%</td>\n",
       "                        <td id=\"T_fe3f3_row15_col10\" class=\"data row15 col10\" >9.43%</td>\n",
       "                        <td id=\"T_fe3f3_row15_col11\" class=\"data row15 col11\" >14.88%</td>\n",
       "                        <td id=\"T_fe3f3_row15_col12\" class=\"data row15 col12\" >5.35%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row16\" class=\"row_heading level0 row16\" >PCAR</th>\n",
       "                        <td id=\"T_fe3f3_row16_col0\" class=\"data row16 col0\" >0.89%</td>\n",
       "                        <td id=\"T_fe3f3_row16_col1\" class=\"data row16 col1\" >3.41%</td>\n",
       "                        <td id=\"T_fe3f3_row16_col2\" class=\"data row16 col2\" >0.60%</td>\n",
       "                        <td id=\"T_fe3f3_row16_col3\" class=\"data row16 col3\" >2.76%</td>\n",
       "                        <td id=\"T_fe3f3_row16_col4\" class=\"data row16 col4\" >0.34%</td>\n",
       "                        <td id=\"T_fe3f3_row16_col5\" class=\"data row16 col5\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row16_col6\" class=\"data row16 col6\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row16_col7\" class=\"data row16 col7\" >4.19%</td>\n",
       "                        <td id=\"T_fe3f3_row16_col8\" class=\"data row16 col8\" >7.87%</td>\n",
       "                        <td id=\"T_fe3f3_row16_col9\" class=\"data row16 col9\" >1.22%</td>\n",
       "                        <td id=\"T_fe3f3_row16_col10\" class=\"data row16 col10\" >0.80%</td>\n",
       "                        <td id=\"T_fe3f3_row16_col11\" class=\"data row16 col11\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row16_col12\" class=\"data row16 col12\" >5.57%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row17\" class=\"row_heading level0 row17\" >PSA</th>\n",
       "                        <td id=\"T_fe3f3_row17_col0\" class=\"data row17 col0\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row17_col1\" class=\"data row17 col1\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row17_col2\" class=\"data row17 col2\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row17_col3\" class=\"data row17 col3\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row17_col4\" class=\"data row17 col4\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row17_col5\" class=\"data row17 col5\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row17_col6\" class=\"data row17 col6\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row17_col7\" class=\"data row17 col7\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row17_col8\" class=\"data row17 col8\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row17_col9\" class=\"data row17 col9\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row17_col10\" class=\"data row17 col10\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row17_col11\" class=\"data row17 col11\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row17_col12\" class=\"data row17 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row18\" class=\"row_heading level0 row18\" >SEE</th>\n",
       "                        <td id=\"T_fe3f3_row18_col0\" class=\"data row18 col0\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row18_col1\" class=\"data row18 col1\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row18_col2\" class=\"data row18 col2\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row18_col3\" class=\"data row18 col3\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row18_col4\" class=\"data row18 col4\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row18_col5\" class=\"data row18 col5\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row18_col6\" class=\"data row18 col6\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row18_col7\" class=\"data row18 col7\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row18_col8\" class=\"data row18 col8\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row18_col9\" class=\"data row18 col9\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row18_col10\" class=\"data row18 col10\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row18_col11\" class=\"data row18 col11\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row18_col12\" class=\"data row18 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row19\" class=\"row_heading level0 row19\" >T</th>\n",
       "                        <td id=\"T_fe3f3_row19_col0\" class=\"data row19 col0\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row19_col1\" class=\"data row19 col1\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row19_col2\" class=\"data row19 col2\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row19_col3\" class=\"data row19 col3\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row19_col4\" class=\"data row19 col4\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row19_col5\" class=\"data row19 col5\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row19_col6\" class=\"data row19 col6\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row19_col7\" class=\"data row19 col7\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row19_col8\" class=\"data row19 col8\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row19_col9\" class=\"data row19 col9\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row19_col10\" class=\"data row19 col10\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row19_col11\" class=\"data row19 col11\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row19_col12\" class=\"data row19 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row20\" class=\"row_heading level0 row20\" >TGT</th>\n",
       "                        <td id=\"T_fe3f3_row20_col0\" class=\"data row20 col0\" >6.02%</td>\n",
       "                        <td id=\"T_fe3f3_row20_col1\" class=\"data row20 col1\" >4.61%</td>\n",
       "                        <td id=\"T_fe3f3_row20_col2\" class=\"data row20 col2\" >7.40%</td>\n",
       "                        <td id=\"T_fe3f3_row20_col3\" class=\"data row20 col3\" >5.03%</td>\n",
       "                        <td id=\"T_fe3f3_row20_col4\" class=\"data row20 col4\" >7.70%</td>\n",
       "                        <td id=\"T_fe3f3_row20_col5\" class=\"data row20 col5\" >8.05%</td>\n",
       "                        <td id=\"T_fe3f3_row20_col6\" class=\"data row20 col6\" >13.05%</td>\n",
       "                        <td id=\"T_fe3f3_row20_col7\" class=\"data row20 col7\" >16.65%</td>\n",
       "                        <td id=\"T_fe3f3_row20_col8\" class=\"data row20 col8\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row20_col9\" class=\"data row20 col9\" >3.79%</td>\n",
       "                        <td id=\"T_fe3f3_row20_col10\" class=\"data row20 col10\" >0.25%</td>\n",
       "                        <td id=\"T_fe3f3_row20_col11\" class=\"data row20 col11\" >2.70%</td>\n",
       "                        <td id=\"T_fe3f3_row20_col12\" class=\"data row20 col12\" >3.70%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row21\" class=\"row_heading level0 row21\" >TMO</th>\n",
       "                        <td id=\"T_fe3f3_row21_col0\" class=\"data row21 col0\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row21_col1\" class=\"data row21 col1\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row21_col2\" class=\"data row21 col2\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row21_col3\" class=\"data row21 col3\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row21_col4\" class=\"data row21 col4\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row21_col5\" class=\"data row21 col5\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row21_col6\" class=\"data row21 col6\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row21_col7\" class=\"data row21 col7\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row21_col8\" class=\"data row21 col8\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row21_col9\" class=\"data row21 col9\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row21_col10\" class=\"data row21 col10\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row21_col11\" class=\"data row21 col11\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row21_col12\" class=\"data row21 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row22\" class=\"row_heading level0 row22\" >TXT</th>\n",
       "                        <td id=\"T_fe3f3_row22_col0\" class=\"data row22 col0\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row22_col1\" class=\"data row22 col1\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row22_col2\" class=\"data row22 col2\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row22_col3\" class=\"data row22 col3\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row22_col4\" class=\"data row22 col4\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row22_col5\" class=\"data row22 col5\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row22_col6\" class=\"data row22 col6\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row22_col7\" class=\"data row22 col7\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row22_col8\" class=\"data row22 col8\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row22_col9\" class=\"data row22 col9\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row22_col10\" class=\"data row22 col10\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row22_col11\" class=\"data row22 col11\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row22_col12\" class=\"data row22 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row23\" class=\"row_heading level0 row23\" >VZ</th>\n",
       "                        <td id=\"T_fe3f3_row23_col0\" class=\"data row23 col0\" >11.37%</td>\n",
       "                        <td id=\"T_fe3f3_row23_col1\" class=\"data row23 col1\" >12.39%</td>\n",
       "                        <td id=\"T_fe3f3_row23_col2\" class=\"data row23 col2\" >10.48%</td>\n",
       "                        <td id=\"T_fe3f3_row23_col3\" class=\"data row23 col3\" >12.49%</td>\n",
       "                        <td id=\"T_fe3f3_row23_col4\" class=\"data row23 col4\" >10.28%</td>\n",
       "                        <td id=\"T_fe3f3_row23_col5\" class=\"data row23 col5\" >12.48%</td>\n",
       "                        <td id=\"T_fe3f3_row23_col6\" class=\"data row23 col6\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row23_col7\" class=\"data row23 col7\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row23_col8\" class=\"data row23 col8\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row23_col9\" class=\"data row23 col9\" >3.63%</td>\n",
       "                        <td id=\"T_fe3f3_row23_col10\" class=\"data row23 col10\" >8.03%</td>\n",
       "                        <td id=\"T_fe3f3_row23_col11\" class=\"data row23 col11\" >6.16%</td>\n",
       "                        <td id=\"T_fe3f3_row23_col12\" class=\"data row23 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "            <tr>\n",
       "                        <th id=\"T_fe3f3_level0_row24\" class=\"row_heading level0 row24\" >ZION</th>\n",
       "                        <td id=\"T_fe3f3_row24_col0\" class=\"data row24 col0\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row24_col1\" class=\"data row24 col1\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row24_col2\" class=\"data row24 col2\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row24_col3\" class=\"data row24 col3\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row24_col4\" class=\"data row24 col4\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row24_col5\" class=\"data row24 col5\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row24_col6\" class=\"data row24 col6\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row24_col7\" class=\"data row24 col7\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row24_col8\" class=\"data row24 col8\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row24_col9\" class=\"data row24 col9\" >7.02%</td>\n",
       "                        <td id=\"T_fe3f3_row24_col10\" class=\"data row24 col10\" >0.00%</td>\n",
       "                        <td id=\"T_fe3f3_row24_col11\" class=\"data row24 col11\" >3.22%</td>\n",
       "                        <td id=\"T_fe3f3_row24_col12\" class=\"data row24 col12\" >0.00%</td>\n",
       "            </tr>\n",
       "    </tbody></table>"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x7f8d045c6f70>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "w_s.style.format(\"{:.2%}\").background_gradient(cmap='YlGn')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Plotting a comparison of assets weights for each portfolio\n",
    "\n",
    "fig = plt.gcf()\n",
    "fig.set_figwidth(14)\n",
    "fig.set_figheight(6)\n",
    "ax = fig.subplots(nrows=1, ncols=1)\n",
    "\n",
    "w_s.plot.bar(ax=ax)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
